{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification, and scaling up our networks\n",
    "\n",
    "In this guide we introduce neural networks for classification, and using Keras, we will begin to try solving problems and datasets that are much bigger than the toy datasets like Iris we have used so far. In this notebook, we will define classification and introduce several innovations we have to make in order to do it correctly, and we will also use two large standard datasets which have been used by machine learning scientists for many years: MNIST and CIFAR-10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification\n",
    "\n",
    "Classification is a task in which all data points are assigned some discrete category. For regression of a single output variable, we have a single output neuron, as we have seen in previous notebooks. For doing multi-class classification, we instead have an output neuron for each of the possible classes, and say that the predicted output is the one corresponding to the neuron which has the highest output value. For example, given the task of classifying images of handwritten digits (which we will introduce later), we might build a neural network like the following, having 10 output neurons for each of the 10 digits.\n",
    "\n",
    "![classification](https://ml4a.github.io/images/figures/mnist_2layers.png)\n",
    "\n",
    "Before trying out neural networks for classification, we have to introduce two new concepts: the softmax function, and cross-entropy loss."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Softmax activation\n",
    "\n",
    "So far, we have learned about one activation function, that of the sigmoid function. We saw that we use this typically in all the layers of a neural network except the output layer, which is usually left linear (without an activation function) for the task of regression. But in classification, it is very typical to output the final layer outputs through the [softmax activation function](https://en.wikipedia.org/wiki/Softmax_function). Given a final layer output vector $z$, the softmax function is defined as the following:\n",
    "\n",
    "$$\\sigma (\\mathbf {z} )_{i}={\\frac {e^{z_{i}}}{\\sum _{i}e^{z_{i}}}}$$\n",
    "\n",
    "Where the denominator $\\sum _{i=1}e^{z_{i}}$ is the sum over all the classes. Softmax squashes the output $z$, which is unbounded, to values between 0 and 1, and dividing it by the sum over all the classes means that the output sums to 1. This means we can interpret the output as class probabilities. \n",
    "\n",
    "We will use the softmax activation for the classification output layer from here on out. A short example follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z = [0.0, 2.3, 1.0, 0, 5.3, 0.0]\n",
      "y = [0.004629   0.04617051 0.01258293 0.004629   0.92735955 0.004629  ]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def softmax(Z):\n",
    "    Z = [np.exp(z) for z in Z]\n",
    "    S = np.sum(Z)\n",
    "    Z /= S\n",
    "    return Z\n",
    "\n",
    "Z = [0.0, 2.3, 1.0, 0, 5.3, 0.0]\n",
    "y = softmax(Z)\n",
    "\n",
    "print(\"Z =\", Z)\n",
    "print(\"y =\", y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We were given a length-6 vector $Z$ containing the values $[0.0, 2.3, 1.0, 0, 8.3, 0.0]$. We ran it through the softmax function, and we plot it below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 6 artists>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(y)), y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that because of the exponential nature of $e$, the 5th value in $Z$, 5.3, has an over 90% probability. Softmax tends to exaggerate the differences in the original output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical cross-entropy loss\n",
    "\n",
    "We introduced loss functions in the last guide, and we used the simple mean-squared error (MSE) function to evaluate the performance of our network. While MSE works nicely for regression, and can work for classification as well, it is generally not preferred for classification, because class variables are not naturally continuous and therefore, the MSE error, being a continuous value is not exactly relevant or \"natural.\" Instead, what scientists generally prefer for classification is [categorical cross-entropy loss](https://en.wikipedia.org/wiki/Cross_entropy). \n",
    "\n",
    "A discussion or derivation of cross-entropy loss is beyond the scope of this class but a good introduction to it can be [found here](https://rdipietro.github.io/friendly-intro-to-cross-entropy-loss/). A discussion of what makes it superior to MSE for classification can be found [here](https://jamesmccaffrey.wordpress.com/2013/11/05/why-you-should-use-cross-entropy-error-instead-of-classification-error-or-mean-squared-error-for-neural-network-classifier-training/).  We will just focus on its properties instead.\n",
    "\n",
    "Letting $y_i$ denote the ground truth value of class $i$, and $\\hat{y}_i$ be our prediction of class $i$, the cross-entropy loss is defined as:\n",
    "\n",
    "$$ H(y, \\hat{y}) = -\\sum_{i} y_i \\log \\hat{y}_i $$\n",
    "\n",
    "If the number of classes is 2, we can expand this:\n",
    "\n",
    "$$ H(y, \\hat{y}) = -{(y\\log(\\hat{y}) + (1 - y)\\log(1 - \\hat{y}))}\\ $$\n",
    "\n",
    "Notice that as our probability for the predicting the correct class approaches 1, the cross-entropy approaches 0. For example, if $y=1$, then as $\\hat{y}\\rightarrow 1$, $H(y, \\hat{y}) \\rightarrow 0$. If our probability for the correct class approaches 0 (the exact wrong prediction), e.g. if $y=1$ and $\\hat{y} \\rightarrow 0$, then $H(y, \\hat{y}) \\rightarrow \\infty$.\n",
    "\n",
    "This is true in the more general $M$-class cross-entropy loss as well, $ H(y, \\hat{y}) = -\\sum_{i} y_i \\log \\hat{y}_i $, where if our prediction is very close to the true label, then the entropy loss is close to 0, whereas the more dissimilar the prediction is to the true class, the higher it is.\n",
    "\n",
    "Minor note: in practice, a very small $\\epsilon$ is added to the log, e.g. $\\log(\\hat{y}+\\epsilon)$ to avoid $\\log 0$ which is undefined.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MNIST: the \"hello world\" of classification\n",
    "\n",
    "In the last guide, we introduced [Keras](https://www.keras.io). We will now use it to solve a classification problem, that of MNIST. First, let's import Keras and the other python libraries we will need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten\n",
    "\n",
    "from keras.layers import Activation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are also now going to scale up our setup by using a much more complicated dataset than Iris, that of the [MNIST](http://yann.lecun.com/exdb/mnist/), a dataset of 70,000 28x28 pixel grayscale images of handwritten numbers, manually classified into the 10 digits, and split into a canonical training set and test set. We can load MNIST with the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "num_classes = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what the data is packaged like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 train samples, 10000 test samples\n",
      "training data shape:  (60000, 28, 28) (60000,)\n",
      "test data shape:  (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('%d train samples, %d test samples'% (x_train.shape[0], x_test.shape[0]))\n",
    "print(\"training data shape: \", x_train.shape, y_train.shape)\n",
    "print(\"test data shape: \", x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at some samples of the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x12cc06da0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vKHp73nnnlW1MdaG6atXJiddffx0I20LKpBRWKQXXXHMNEGon45kQSUK1tcrGyId9990XCMeozh8yISkGRx55ZEE/T+0mVFOt+0lC59nqc//zzz8P1DQUyoXmQbWLKCcXX3wxENpn1BetNWVyA/DQQw8B8MYbbzRydHUj/4tS+GA8/PDDQFAik8jBBx8MVM0FDz74IBDOc5o/VRuq86HMa1RvmiS0tpeaKtVchlxa32s9MWbMGCAYtskEqZRYiTTGGGOMMcYYkzdNQokcNmwY3bt3B7I7cTX0fpKQnbZuhRqcd+rUKR1lUURC9TFJqpHccccdgRB1UR2hIklxVIOgiKhUHqkkuhXV67d69+4NBOfGJNCyZUsATjrpJCB3jYZcTJPEpZdeWufz22yzDQADBw7MePznn39ORDQ6F1KvpHLLyTPuwpoLqeOKfpcSqYwagxqeb7nllkDVb9HvOfroo4HszntS7aSCVRKqJ6kUlHmg//mbb74JBGc+bVedo6Qsq32Vjk3VVH777bdA5bfhUS2kFIbq7UAgv3rKpCDVWDXWypiR+6vOCXq8HBk1coFv27Zt+jEpIKoBrGTuvPNOICj1yoyJZ8ioDm3TTTcFSttiq5z07du33EPIitZQWt+NHz8+nZGh2kjV+wup/QMGDABCbWQSajuFWgdpjSzfEPkaaM7T+kQ+GsrOaNasGRBquEtB8q6UjDHGGGOMMcYkligJvV6iKGrUIDbaaKN0k1+5RuZb85jrvhwOKwVFKJQrrRxqRdHKqUgqoilFRL3AVH+lHlnq2bnTTjsBwZVWEfdc+6zUlN9//z1ddyl3uCSgGlbVl2RDz8vBLon1nXGkQN5+++1AzV6Y5513HmeeeWbJx9VY1Bx+gQUWqPV5/SYpC0KqknqESnUvJ+qXuPvuu6cjuhqf+oHqGJP6JTWzEhUIzR/K0pACkauZfaWiOl4pCVLolK2ThHN+fZALuepx5Uyp36HzSJL6CWu+UBbN6quvnnErdXyFFVbIeN+rr76a8Topf9VrquUdoL6gxVJg5QquYz+KonTPwbibcy60LpsyZUr6syDUhPbq1avxA86CtsUzzzwDhHVEfK0nj4J4b1+tqeTsXxtbbLEFENZdlYx6eWr76zhTHabOEeXsgay5+7bbbks/pn1KWUPK/lFWns7FqqM87rjjAFhvvfWAmj0ak4AyCS+55BIg+IhkQxl3O++8cyF6So5LpVLr5XqRlUhjjDHGGGOMMXnTJGoix44dm46WKEIm97BsfSB1X453cupStFbKZqWhmgr18lGkT/Uy5XTqk8OUIsdC+d6PPvooECJdyu8WUlFHjRpV6/sVkR08eDBQ1Tft8MMPB0jfVhLahytBgRR33HEHELaJmDx5MhD2x0ojl5Kt2k/VMMi5T0psvD6jnLz99tsZt5C97lb7YCUqkEKO3ZrzK6l2riHIPVLqstwKNe9+8MEHJRuL6hilZnXr1g0IrqzaFlI5qm8bjVseAPFMFLnPllOBXGqppYDg9CgnS6mn2Wqos9UeS+3SNpo0aRJQ5Qeg2li5j0vNLNb+HO8r+8477zR4HlCdbjYPimKiuVsZCNqPDjrooIzXrb322hm3Ih/H/qagQArtw/HfLaWunAqkUJ1q9eNH20A1q9nO2TqOVE+ouakczqa5eO2114CwJlYmgFDWibLbtGY++eSTC6FE5oWVSGOMMcYYY4wxeZOc8HgjSKVS6WhJjx49gHAFX19Uk1DfPkhJY9iwYRn3FQFOUs+wJ554Agg96tR7MK5AfvzxxwCcdtppQOjJFEc57tVJUl871cdIKciFIqdJpkWLFkCINCvKq9x8KXSqr1AUHUKd7nzzzQdQsshZMVDUUwpC9R5iSUY9CbfffnsgKAMvvfQSAJdffnl5BlYA9FukwGkbPf3002UbUynQsRevW9P8qayUYiIHZjmpZssI2nbbbbM+ny17SAq6XLrLger1lcEkVUNjVS/SiRMnAsGbQP3ftA1GjhwJhF6f2bJO5L5YSuJ9qUePHp3+Xfmi84Hmw3if7nPOOacgY80HuXIK1e3H1eBDDjmk1vdr7SRH0AMOOCCtqDcFdthhByD0RNY20jn7mGOOKc/AqqGawOOPPz7j8VdeeSV9TGZTy+VnoGwpoWMyyajOM551sfPOOwPw7LPPAiGTYbPNNku7sL/wwgtFHZuVSGOMMcYYY4wxeVPRSqTUjWnTpjVagRRSs1RHU+monkQ1GuVEvW8UrVXNjurGpNTF6yQefvhhoKYCqSinopl77LFHxvtfeOEFhg8fXtgf0QhUH6Soc1Pg9NNPB0LNqfocaZtcddVVGa9fYIEF0tGzoUOHAqFeMJvzaX2Rk5kip3LllNL9zz//FOR7qrPmmmsCwZ1WvPXWW0Do7Zck5ptvvnQWgJQCKalyBE6Cm2xDkQut3BlfeeUVIJnbohjccsstAOyzzz4ArL/++kX/TtUxxns5ivrcz/ac5lH1wbzwwgsbO+x6I0dHuavKi+D//u//gBD9nzBhQsb75EwulUd1nZVU914fNB9mczatr7JZSF588cVaH1cWRjak8EDo9VzJaF9WTaD6KQplbmjfLSfK4lIPU83pO++8c8563cceewwI607dl49BJSKfkJdffhkIx9k888xTI6OvWFiJNMYYY4wxxhiTNxWtRCoy2aNHj0YrkOLYY48F6nbiqiRUk6E+TeVE9Yl9+vQBYKWVVgKCYvDVV18BoS/TF198AQQ1VbVNUhwPPfRQIDjkCUXUkubIqgh6LpTfPnXq1GIOp1EomqdtIaTkyzUszi233JLuQyUKrQypP54UTimSOqZPOeWUdN1YoYjXdgmpoklUGq677roaToQXXXQRkMyeWSb5SCHQcaCaMWUsCJ1nNY/Ej5vq9+PPKWPhvPPOA+Cmm24CgsNrKVDPQWU56PjOpoYst9xyQMhYqERatmzJ3HPPDeTO5lB/OymzcXr27AlURj2aaN++PRDcP5sKhx12GACLLrpoxuNSZGvzmigXUtc0J8hXo7aMGfV4V0aGejjLKV5+DZW81m/VqhUQakT1f5kwYUJ67V9sGqxERlG0dBRFL0RRNCGKog+jKOo/+/GFoygaGUXRJ7NvFyrccI0xxhhjjDHGlJPGKJH/A05IpVJvR1HUEhgXRdFI4EBgVCqVuiiKopOBk4GiJI4XsseQImfxPkaVjvLdk/B71JNTdVerr746ECJF8RoY1XF+/vnnQPbeWkLuaYpQF1ptaizqR5SLH3/8EYA///yzmMNpFKonkMJ9wgknANn7DZYS1d7Ga1ukfnTq1CkduWtsj7lVV10VgCOPPDLjcdX6FCpDopCo15T+TxB6f919991lGVMxUOTZVLHQQlXxXGVEFLO/oubo/fffHwjZFUJ9z+QOHH8fhFp4qZmdOnUCQh87vVZ1mKqxLgU6l+WLlMi42lNJ9OnTJ13rHnf+jaPzwsILL5zxuM4bcff4SkCZLfGawUrlwQcfBGpmE4lrrrkGKG/dapxPPvkECMe+jqfWrVun6wNVAz9kyBAgrO2lQGodpjrmSkL73vLLLw8Ezwntm1rznnDCCXz55ZclGVODryxSqdQ3qVTq7dl//wJMBJYCdgNun/2y24FujR2kMcYYY4wxxphkUJCayCiKlgPWBl4HlkilUt/MfupbYIlCfEdtKBpx//33p3Ps6xv5l8PrhhtumPGZSc6TPuCAAzLux/veVEdRptGjRxdzSHnx7bffAqGHmP73DUU1lPptZ599NpA7Spp03n///XIPISu9evUCQmT9m2+qDnXVrSoipgi0+keKDTbYoMZnFrp36RtvvAGEuiX1QBTbbbdd+jn1mqvrGKoN1UBK/VDk/d133wXg4IMPBsI+miRUR1JdFZHTZVNC+6h45513yjOQerDRRhsVTL1WDZf47LPPAPjpp58K8vm1oTp1Zb6oRlLHoHrz6fF4L0ipo6eddhqPPPJIrd+hPnb6LGUY3HXXXUDoqZZktC3k3pxEtE2qZ2dddtllQPZeo6qfUw2s3quatWw1kpVE9ayuQmbDlQplz2htqGNQSp6OpySqxffddx8Q5hk5ru+3337p8atnq5D6rXVzLhfXUqDOAuozu/jii2fcl6Ko3pfK2tPrpLYK1Sgre0Prm1LQ6IvIKIoWBB4Cjk2lUj9XP6hSqVQqiqJacw+jKOoL9G3s9xtjjDHGGGOMKR2NuoiMomheqi4g706lUg/Pfvi7KIraplKpb6IoagtMr+29qVRqKDB09ufUXuSW+/uBKhVgzJgxQKhV0BW9UHRFfVTiEVDdl4qlvpNJ5LbbbgOo4axYHUVd5GYlF7kk8OSTTwJwyCGHAKEmMl9Ut6VtmnSHN0WRttpqqzpfpz5McfUuCaieSgqbnM9UX6coYH34+uuvgfqrgLlQr8pTTz0VgM6dOwOhFyIE5VCqeFy9UB3Wxx9/DIS+sUcddRQQ6rI0f6huRP3xxo8fX7DfUyh0vGl/hKB6lzJyWS7efvvtcg8hK1tvvTVQ1bNMtTrqiyvlOF/VSn134z1a1RNUGSHFRJk8Ugvj59n47QUXXACEno91qYmKtnfrVlUpo5pI1XomeTvH1b0kI+VeitW///6bVkakAMlBXejcFd++qpnP1puxkqiepRav+UwycgbW8SN07h4wYAAQ3O2TyLhx44BQ76414Kabbpp29ZfqLZ+Ce++9F0hWdqF69iqrQijDKV9UI6q5/uqrry7A6OpHY9xZI+BmYGIqlbq82lPDgd6z/+4NPNbw4RljjDHGGGOMSRJRNqfLnG+Mok2Bl4H3AV3iD6KqLvIBYBngC6BHKpWqUypqqBIp16UxY8akowzKV2/ofSkMSXRVFBpr//79gcyIsxRI1WopcrPJJpuUcoimGuqD+eabb9b5OtUAxfsoJgE55Y4dOxaAJZdcssGfJQWye/fuQPGPNY1V/Un79++fM4Ks3pWqH8v2e5UBIUVz5MiRjR9wkVAvO2UnABx44IEA3HnnneUYUlHRb1K9vHpqFbovaSFQFP3ss8+uUW+m+V5q8eOPPw4Ex1P1yZXyLlVTPf2E+qcWuga5tu+44YYbMsYeP8/KOVEKpJT/xnxn3M01SajH8SuvvAKEfVHZKUlU6FR/pTVEu3btsjqji3h/UO2jUox++OGHooy1FKjGWupPdc4//3wgOKtr/04SygCTM7K2lc6LSVYgmxrKBpKfR7b1iNYfctIVH374IRCyS4o0541LpVLr5XpRg9NZU6nUK0C2nAx7qxtjjDHGGGNME6TBSmRBB9FAJVKMHTs27a4ar73Idl8KgqKX8RrKJKMohKK6U6dOBar6aKnuS3VmqtGaMWNGqYdpZqMazmyOdkLbavDgwUUfU0PRcVa9rq46qrtQ7WCc66+/Pl0nJRfVUtOqVav0+M4880wg1HjmQjUH6pcmp7ck9/RUTajqNDUXfv7552y22WZA5Tsa14Yc7vR7mzdvXs7h5MU888yTrpORE2abNm0a9FmqFVdPVM1DxawN2nTTTYGgRK6yyipA6B+rbIt438g5hSOOOAIILrRSJlXHnUQ0pz///PM5eyRqfaVMKP3epoCcx3V+PuaYY9LPSWl/6qmnANhll11KPLrs7LDDDkCorRY6lymjzZQeZVOqs4BqPXVfivbPP/9chtHlp0SWvwO9McYYY4wxxpiKoUkoke3bt08rJA888ABQsxZDUV2pH6rDSmIft1woeqEaGLnxvfjii+nei0nuPzWnIbV7t912q/V59QzbYostgFAzaEwhUFaCFC7N+V26dEm7WTdFpETqHCCH3XIp4PVFDsJSzVu0aAEEdW+bbbap9X1yyFStfCWe40zyWGedddI139kUSbm23nTTTUAyevIVGvXo69y5c9rxWsjV/7vvviv5uLKhLICDDjoo43HdnzhxIuA1o6mBlUhjjDHGGGOMMYWlSSiRxiSZ9u3bAzBq1CggODFOmTIFCHnw8Z5bxhQCKVJStFQf3blz50RFzAuNlEjVMinyrppcY4xp6qgmUq5aNaOPAAAgAElEQVTOQvWrmg/VA9qY2ViJNMYYY4wxxhhTWKxEGmOMMcYY08RQj2f1gZRHg1A/3VmzZpV2YCbpWIk0xhhjjDHGGFNYrEQaY4wxxhhjjAErkcYYY4wxxhhjCo0vIo0xxhhjjDHG5I0vIo0xxhhjjDHG5I0vIo0xxhhjjDHG5M085R6AMcYYY4xpWmy33XYAPPPMMwDccMMNAPTr1w+Av//+uzwDM8YUBCuRxhhjjDHGGGPyxi0+jCkCLVu2BODmm29mjz32yHiuc+fOAHz//fcATJ8+vbSDM6YCiaIIIH08/fLLLwDsvvvuAKy22moAbLrppnV+zk033QTA2WefDcDXX38NQBLOhcY0JXbYYQcAnnjiiYzHl1hiCSCcA40xicMtPowxxhhjjDHGFBYrkcYUkMUXXxyAiy66CID99tsv62t//PFHAP7zn/8A8PHHHxd5dMZULm3btgXgq6++Kujn9u/fH6hSKP/444+CfrYxcyIdOnQA4LnnngNg6aWXBoLyuPLKKwMwa9asMoyudgYPHgzAwIEDATjooIMAuO2228o1JGPKiZVIY4wxxhhjjDGFxe6scwgLL7wwAJMmTQJgvvnmA0LE8LvvvivPwOpB69atAVhyySWB8FvqQhHQVq1aAfDhhx8WaXRV7L///kDdCqRYZJFFALjnnnsAWG+9nEGfxNGsWTMAtt9+ewCOP/54ADp16gTASy+9BMChhx4KwE8//VTqIdaJjouePXsCMGjQIADatWtX6+s1fm2zcePGATB69GgAPv/882INdY5HWTN//vknAM2bN894/p9//gFyqxuaR+aee24AhgwZAkDfvn3T9ZQ///xzgUZtGstZZ50FwOabbw7AFltsUa/3q5bWlA7Npzr/ilGjRgHJUiBVU33iiScCYZ7R/NDUufLKK4Hwf9hoo40A+Oabb8o2JlM5WIk0xhhjjDHGGJM3c5wSudhiiwFBObn66qsznpcyN2PGjNIOrB5IPVEtXZs2bQDYeuut05H0V155BYBll10WgBYtWgBB/RJyT0ty3r/GeMEFFwBhGykyrd8odVXsuOOOaSfHjz76CIB11123qGOdMGECABMnTgSCIlcXCy20EBDUyylTpgAwZsyYYgyxoGibaOxXXHEFAH369AHCb9hzzz2Bqgj0iy++CJT3GNtrr70AOO+88wBYaaWVgBCFzlYrrmPtiCOOyHhcv6lr166FH6wB4NtvvwXg7rvvBmDnnXcG4JxzzgFCvdWDDz5Y5+d0794dgFNPPRWAVVddFahyd5UqefTRRwPw3//+t2Djbyg9evQAwj4ppeDYY48F4PXXXwfCsSflTa/X/bFjxwKFryltDFIZxZlnnlmwz1Z2QJJYc801ATjuuOMAmH/++QFYccUVgVD3u88++wDJ/A11ob6QqisUqv9XpkoS0DlAWSU6TkaMGAHArbfeWp6BlRipxe3btwfgwAMPBODCCy8s15AKgrLPNKcoA2X99dcHQiaLzunKIhs5cmRJx1npWIk0xhhjjDHGGJM3TdadVVGV5ZZbDoABAwYAoZ5iwQUXrPV9//d//weESGGSkAI5dOhQgBr9ByFEzqdNmwYEF7Rs3HzzzUCoWUsiUvdWWWWVOl8Xj8BXZ9iwYUCo1Sg2O+20E1B7zzqpWNoH//3334zn3377bQBOOukkIKhcSULqsKK4cri84447Ml7XpUsXICjjEOpSd9llF6D0dYQtWrTg5ZdfBmCttdYCwja45ZZbgNA7ULdS8hWl3nvvvYEQ1ZSapd+vHoSVhlSuM844AwjR2ziqdX3ssceAsE3Vu7GYSIFUDXFczaovUjIPP/zwdKaGnBnj+3Mp0Tno0ksvBcI+OtdcczXovpRIZbCUE22zxiiP8WNMql0S1Tvtq5ovlU2TjS233BJI5txfG5onNN411lgj4/n7778fCAprOdEaUHOXejo//fTTQKgN/Ouvvxr9Xeussw4QzvlJXGdpntCaURka5Zz7GsI881QlVu67775AmB+ktKrOXQrzNttsA4T+wsqMUg9TY3dWY4wxxhhjjDEFpskokQsssAAQIsiKcEq9q/ZdQPaaJ5FEZy5Fpi+77DIgRFYUqR4yZEg6qvT3338DIe971113BeDOO+/M+Mz33nsPCIpMklBESC5h2mZSh955552M12vbqv7xs88+47PPPgPghRdeAOB///tfkUedm8MPPxwIv0+RvzhvvfUWQFo1UzRXSmU5Ub2EanqkRMbRvimF/Msvv0z/Xjn1lTo627179/S4llpqKSCovjq28kXHl44rZQcoKppkmjdvnnYk7Ny5MxDqhHLNj/F5VMfX6aefDsBrr71W+AEXme7du6frLT/55BMgRKs155QCRc7vu+8+IKj5mtuz1Tzme7/UWRm1kUuJHD16dFqNq2S23XZbIDgAK5tGzuJvvPEGAL/99hsQnMflmFkpSqRUPWUJCJ2rpH6Xsw+rskVeffVVICiQ11xzDRD2xZkzZzb4O5ZZZhkgqJpSnJ999lkgZN8kgaOOOgoIviDq6ak5r5Jo27ZtOkNvxx13BMJ21LpZv0+19fLSkAeFXL/ltaFjMwlonaFMGZ0b4plumuvffvvttO9LI7wnrEQaY4wxxhhjjCksyQ+X50Hz5s3T0VVF/poicWeze++9FwgOk7WhaFs2py2pdklC9ayK3im6op583bp1A0IOf6Vx/fXXA7mVSNXR6HbDDTcEQgSx2D0v60IKZC6kdFVHPcJUe1JqVl111bQC+cEHHwD1VyCFopfXXnstUHudclJZe+21C1a7KdXolFNOAYKSV9v2TyoPPvggq6++OhB+h/4/ffv2BXIrtIVAUeYNNtgACAqkbnv16pUxllzKY9zNNQnZR9nQ/7uxda7FQk6Ov/76K5A9s0XKo+Zq/c8112u+idfdyRmzUpCngtQfIbVHmWHlVCCVjaZaOK2JVAt97rnnAqHPbEPQ+VBqlxTIJ598EggZHklCqrH2TW2zSkLK3IABA9h6660BePzxxwHo3bs3ULMnqVS9+ByjntfKBiglcmWWCqwx6LwqhVtu/1r7XXzxxRmfo9ftsssu6f1amW/FwkqkMcYYY4wxxpi8aRJK5IABA9L9ibJFWVWzo1ooRXsVlaoEVAMpFWXy5MlZX6t+mHLkk7onPv30UwAOOOCAgo+zsfTr1w8I9SPaplJ7KlWBjPPTTz8BIVqt2oxsqDZKUapyKpFy59xss80a/BmqY9ZtqXryXXfddWn3UbkISoGSMllfVHur+or3338/4/OThPoOKpOhOnLyVIaC6iq++OILAHbbbTcAHn30UaCms7BqNCrVnVZ1UTom5bKsGlfVmhea6nWQ8RpIqbnqA5nvZ0mB1DbVrba/snd0W0qy1UImVYEU6kWsGsY4yoTS/qP/udzhG5rxkBTkwqp9MptyKhfWhs6nhUAK5GmnnQYEJ+3hw4cDhVEghdxXpUBqjaKetMpYSRJx5/4HHnigTCNpOKq17d+/f/qcpUy1bKgHcHztK7X8yy+/LPQw0yj7TOdVjUV9YpXpoF6+cq7XWk/qufbh33//vdbvefnll9M1wMWm0UpkFEVzR1H0ThRFT8y+v3wURa9HUfRpFEX3R1HUrPHDNMYYY4wxxhiTBAqhRPYHJgKtZt8fDFyRSqXui6LoeuAQ4LoCfE8N1GfvsMMOS9eBCKkaigCqDu2QQw4BQl50/H1jxowpxlALwoMPPgiEHm5x51mAxRdfHCDtMqgIh5BbmvrcFaIXUqHo2LEjEHLZ48hFbPDgwQA89NBDQJWyBMFptlJQdPKuu+7KeDyXIpkEvv/+eyDUZ2mbxF0s1adVEbU2bdpw8MEHA0GJLZUCKf7880+mTp0KBIU+XiOZL1ImlNmw/PLLA8nsB6YIq8ZcPWtj/PjxQFAaFeGUw53qKlQrKJUsnvmh5z/++OPC/4ASIJVfyuONN94IFE+BFFINN9hggxo1kPkqkCJeUxnvGynn3Ep00C03uerGpIzof66aSWVC5WKTTTYBSt87N180DwwaNKjW53X8K1OlnMiRXrXAmpN0/imEAinlUaqmzunqo1zOWtBsaG7TPqqaQdWxVxLy+oiiKL22jzPvvPMCwQ0+7miqrDwp1fEaysbQtWtXAPbcc08A9ttvPyBkXz3//PNAOG7UT1vHf33XRtofO3TokL5eKDaNUiKjKGoP7ATcNPt+BGwFKD/mdqBubdkYY4wxxhhjTMXQWCXySmAgoMLCRYCfUqmULMu+ApZq5HfUYNFFFwWqFEiAdu3a8eabbwJBpVPUVVEF9cRSZFkR9HgkPe6IqV59SUB1SIpaqP+NcsFHjBjBHXfcAYQIiNBrFOlMUo6+ojIau+o540hB0a0cE3WrGkpFlpKKFFP15pPqc/vttwMhUiiHvzhSGhT1LofDrvqXKVdfyrccznSMSonUcfT333+nFeetttqqZOOtzqxZs9L/+9GjRwPB0U3ziKLYP/zwQ52fpV5Tcs4VSYhAS4mSkiW1VXPeL7/8ku6Pqd+vnqxxVlhhhYzPiCMFV9kBcq+sNDR+1Z/rf6hjslh9ZpURM9dcc6UVAtWz5yLeV3LjjTcGarq1Snl8+OGHgVB3kwQqtYZW6Lwa75erOTzf3r5SU+LZKUngmGOOyeoEr1qtuFtkOVB9u/736snZp08foHF9IKsz77zzpmt7tSZRxls5a0FzISddOXnedNNNQDKd+rOxzjrrAJmZTspMVCab/AjUm1Mqnc5NUjG1dimUAtmqVat0b2+5riqDQTXRqkMvtK+FlM5FF120ZNkADVYioyjaGZieSqXGNfD9faMoeiuKouRcpRljjDHGGGOMqZPGKJGbALtGUbQjMB9VNZFDgDZRFM0zW41sD9RqpZlKpYYCQwGiKKpX4yqpaOpzA/Djjz8CIRddtVnq2yZlMRdSUi6//HIg9BaSIiFXpHKguiU5oynSpnq0Xr16pXvlCLmESbVKkgLZokULINRPrLXWWhnPK7KuqKyiNspdv+GGGwBYaKGFgOD4t//++xdz2BmotlauWnJpkyNcXUgpqG9thn6v1JFycv755wM1e3rGFX6pI7NmzUpH6ydMmFCqYdZASpPc86RISlF8+umngaBy69gT6vsl9Vxo3qnN+bRUyHVPbntxZ2b9tssvvzzvWi0prtlQjWku5TbpqKZViqOUlbgLbaHR8VL9e6688spaXxvv+yglUqpp/LOkQKp+uZwK5BZbbJH36/TabE6uQr3UdAyXA513pYZI7bj//vvzer9+QxLROW3AgAFplVzo+JBfQRJQNpqc9x977DGg8H4XW221Ffvuuy8QVCx5cCSZeH/mcp6HG4pqBpXh1KVLF0aMGJHxmvha5PXXXweCX0Gx3O1///33tBoaXzcUC6mscnu95ZZbSua63WAlMpVKnZJKpdqnUqnlgL2B51Op1L7AC4A6q/YGHmv0KI0xxhhjjDHGJIJiSBknAfdFUXQe8A5wc6G/QKpP9T4oI0eOBEJ/NkWI8lUghZS6xvS/KzbKYT/yyCOBUAuoHlUQIjUnnHACkEyFYLXVVgNg7bXXznhc9XbahnHkMvnuu+8CIcIoByz9f/Q5hUROX506dQKCwnbVVVdlvK4+ykWxVY5iIqVf0T651On/I8VOz99xxx011Ltyov6OUnE0X6gmUiq5+iSq1kEuyMpcEIp6lwMp+YqGxxVIIYfF+vRbVQaGjrE42WqlSoHqY1R/IlTzIzc+ZQ388ssvQKhL0f+ha9euacdrHedSw+JR7kKj+va55porHUGX4iiXvXiNY677Uo2kjiehBjKbEim1MZfqWNd7y6FEKpsm7gypDKZ8PRVUE6p9U6pJOVHtumrHWrZsmT5XqcZTGTeaB7Xe0JpE7pPKiijFuU7nHKHaeymUqgnO5rgs5/FsaybNDao/g9B/vNRO4w1BmQuVjOpatb9tvvnm6SzEzTffPOO1mv/UF7LYnhl///13yRRIoYwpqe/PPPNMyb67IBeRqVRqNDB69t+TgQ0K8bnGGGOMMcYYY5JFFK9dKssg6lkTKTdPOSmussoqWWuxavmuOl+niHpDIqKlQtFPqR7xOkiAU089FQhRxCSi6OUll1wCBFc15ZPnGzmXsqXIoNxrVStaSJZYYgkg1IA1BqlaqletL1JgyuEEp2ifaupUZ3DzzZmJBzpW5YDWtWvX9GtV85pkpFBK1ZYLXzyirufL5TgL0KNHDyB7PaZq7BS9rQ+q7VKUXyrXO++8A8BOO+0EZHd3LSTa73Wr41wZKvVFUe2WLVumVQah+lIp1sVCtbn33HNPjb6Ojb2v+SVJfSEbsu6QWpft3Bzv+VwKlC1z5513AqE2bptttgFyK5FysZ4yZQoQanFV915OpNipdyyEXnvKemrVqqo9uNTy2tYiELZZKTIWlA0jdVi9X/NFc9j06dNrfV7rFmXbQMhUiDtiKmtq7rnnBmrW0JeSdu3aAWEeUKZKvM61Ell22WV57rnngOAkrvlAWYXqwdiUkIeDsvGk/FfPSmwE41KpVM5Uzsrfe4wxxhhjjDHGlIzy2zs2AOWdq69Nx44dc0YhVfeiiJGiMPErdvVzKSdyWlIU66+//gJCLvsTTzwBhD441VHtVjZnvySh3xXvrZUvioLK2U79FqXwlQM5nWl/nDlzZjrKHEd1ULfddhsQ1JxKQEq3IqvZIqw6Vrt16wbAG2+8ka6jrAQl8o033gDCvKGeU3ElZc011wTg//7v/zJuS9mzVPNBfGyqz1Avt/pw0EEHAaG3WNz5U/0li1lzrVpGuTFrGzRUeYyjOsja+O677wryHblQZsPXX3+dnuelEOjcpb6RmlvU/1OP6/XxvpBJUiBzobrGF198kbPOOqvW1ygLQvWV5XRljdd4qjYu31pI1RorYyMJGVDzzz8/EOoaxYQJE9IOv0J9VXNlYKindymUyNNPPx0IPflUEy1FXu6k8ayDOKp7j9da18Z2222XcRtnxowZQHmVSM1z6vWr9WVToEePHqy44ooZjylDpSkqkEJrX2UwDBw4sORjsBJpjDHGGGOMMSZvKlKJFPvssw8ARx11FCeddBIAiyyyCAB//PEHEFzB1M9OyoB6LKp2QWRTjUqJolbx/oFS7KQ4xJ3AFlhggfR76tt7sNA0a9YsXU+knlnxHlpykG0octDUNpdKIjeucqAefIp+TZs2LatTl6LP9VWr5CopF7lSIpVctS+KhGVzuotz1113pVVyOaHmG7UvB3IolNIoxo0bB4RIvJTKRx99FAjRbzlsSnUvBqp16dOnT8bjqumRcqd6repI5Ys7JMvJsEuXLkCow9YxqxpJzavF+H3rrrsuEGo4e/bsWefr5cyt+VE1MhdddBEQ1D59nhztakOv+e233xo09voitbBnz55pB1z1gVStazZFMa4OS5FMcjaK1FKpjdlUx+roNXH1rxgu3PmiDIvJkycDIaskF1LD5KismlvV85UTnZc09+kYGDRoUI2Mg7iLveqLVVcoB3ado0s55//0009AyNzSrdaKuZAi27lzZyCc+6Qm/vrrr+k1aK5z8ccff1yfoRcFeU6IRx55pEwjKRw65/Xr1y89D+qcrHm/KdK1a1cgZISpJrnUrrBgJdIYY4wxxhhjTD2oaCVSauNll13GXXfdBYQ+KVJGVCNYScQVA0XkjznmGCC4ccrlUr0t+/Xrl65h6tevHxDqAkrNBRdckK75UyRT6ujJJ58MwPXXX9+gz5aCoEiT1JTLL78cKK8SqXodqQFSbKqjuhfVXGg75oscUMtRv6verB9++CFQfxUglUo1yJmx1GyyySZAlVsmhDqSH3/8EQiujHEVWQqt6rSOOOIIAK677jqgOIqdjofFFlss43EpkaoVjvcH69evX1ptUM1j3L1ac4vmHLmzFrPWU06GUmWy1Rn9/PPPAFx99dVAmOtUK63fr/OE1EXNkXEmTZqUzlBRr9lS93B97bXX0oqj+n3G0XZUTXG8L6QyFRSRTzL5KJAi3v9NlLMmUnWDOq6VZZONZZddFoBrrrkGCG7PRx11VLGGWG+kNj711FNAUEvlhg+w9957AzDPPJlLSLk1a1+UEqnfGZ+jkozWLZMmTQLCHC7uu+++tLpZCUi9EtoHKxHtk3LyX2qppdJu/hdccAFQe+ZNU+HEE08EQi1kObNOKvoisjoyQMjXCCFeNJ5klDKjCVsXS/GGxEqpgbDYK9dF5PDhwznwwAOBmsYVSoFRSosWe1qgx+3N1TRcjY3jZkhKJZSpQTFTB3OhdB3dbrjhhjXSdmUso3SZfBeqMhbRBVw5WG655YCGF+VHUZRe9CZ5QaHAjS4ehU682VpZKC1Ntzr+3nvvPSC0RCkkMpKIX5wrgDZy5EggtMTI5yJeaTFqv1NbMKRYaJ7LdvGo/6EMtNT6QSiNTWm+alWghUfcUEMXXX379q2IhYdSpNV+Jp7OWglBmvqg7R1PY9XFYzkvIpW+mS86VtV2QhchSr1OAtqPFIjXcTR+/Pj0uSdb24z4hYrQhamMyioJnQNU0qA1phbylYouQCoRpXGq3OKXX35Jt3QpR0pnqVBauIys1OZP5TXlwOmsxhhjjDHGGGPypskokfVFSkMloEigePbZZ2t93WOPPZZOASu3yvPSSy+lI+ZHH300EFJsZZChlJi4IU7898ZT7KRcKt1GBhRKbysmUjl22GEHIOxH2azLu3Tpko6W1RcpJEpfVRS4nG1o4qpHfenYsWP6MyZOnFiwcRUKmSdoe2rfUzpnvhb8MndRVLSYbRbizeXFzjvvnPN1v/zyCxDSN6XuKcJZDtRAOY4aKSsjIZ6JIQMhZTooTVwlDnHUIF7qUCWokNWJt/TQfc2rSUTpq/HjSAZd1VXFXAqk3lMJqDXGAQccAIR9TenupU6bzgfNWbvtthtQtaaIb4t8kYlLMVsBFRql1cfTyp988kmgKk1e54tStnKak1GJlMzHtJa44YYbEqXmFwu1qdFcr1Zi5cRKpDHGGGOMMcaYvJljlUhFb3WbZGQHLlZYYQWgpoFMdeOB6vWR5ULROUWPFI0cNGgQEGp7VHSvFiAy4JFJgYwxpMyVo5G7kNHIqFGjgFDjochQQxq6i1dffRUIJguyDS+n8hhHufdSg2S0M2HChFpf36xZMwD2339/AA4//PC0otrYFi/FQJF21c0p0vnMM8/k9X7V8cmUQS1fZG5TDGQzf+SRRwKw8cYb1/q6jz76CAjq6FVXXZWeJ5K0j6nFgFDdmcx/VEssq/5FF10UCC2QpCDEkfmaFC5Z9SdRBcqHeEuPctW/50M2BTJe16jXbb755k1CgdQ8om2jel/V70+bNq08A8uDoUOHAiFb4ZRTTqlRI54NtdvROb8S2y106tQJCOcurUc0b4wZMyZtxGUlsrhozpfPhtbt8h6oZJOg+qBMw08++QQIreTKiZVIY4wxxhhjjDF5M8cqkVIYKsHJToqN3AXVfFqqUJs2bQDS7lRQ3Bqs+qL/sdpB6FatOeSUKzVLTXErwWVLUVpZLKt+UVx11VVpN9Y4M2bMAEIzY7nKJrk2S9tIt3LGVdN5tRXQbzv++OMznv/iiy/q3dKklEjVizt4TpkyBQhtWYTaRey6665AcOVt3749EGpoi8m9994LhFpp1enEkdqY9LZHqn0UcnfWsaYodLy9QJy7774bCO6t2oaVqjwKZXbEayKT7HyZrZZYamNd52EpkHEX3kpA54W2bdsC4RiVgpVkNHeptUW8xcWchjKllMlwySWXpLMbTHGR0qi1oeYLZac11C2+0lDLHCmSScBKpDHGGGOMMcaYvImSoMRFUVTyQUgpifcw3GmnnYD8a6BKgRqbKxc/Xs9ZfRuq4arcIZOsapnKRw3c1eA3ruAJHW89evRIK9FJRGq+GtjHf09tx1x1br31VoBEq62msunevTsA99xzDxAUybFjxwKhL+YVV1xRhtHVTjan1bqQ8qg6yUpAv0+ZF+rlqXlDPgDqkWySy9JLLw2EHr+tW7cGYPDgwUBVjWglofHKeXzFFVcEkulNIKTgK+NJSpwUfa0/snkyNBX0f1A2jbIS5XNQJMalUqn1cr3ISqQxxhhjjDHGmLyZY2si40ixS1ItoVBtz3fffQcEV8KuXbtmvO6+++5LR5msQJpSINdBRQK32morIPTqU82IaiXjjsJJQzWt6usWV05eeuklIPSNfPPNN4GgLMgtzphiE6+JVD9aKZFJIl7PmM15VVkKlaQ+Vkd1/C1atACCU/Cjjz4KWIGsJKZOnQrAQgstVOaRFIbHH38cyN7TOomo17FUU6HzcFNXIIXUb/kCFFmBrBdWIo0xxhhjjDHG5M0cWxMpNU954sq57tmzZ6mHYowxxuSFnH/lyisFUlkBAwcOLM/ATBrVo8pJXb1b5xQXSWMKgfouK7vi2muvBWDAgAFlG1M5mD59OhD6QmZz/C8wrok0xhhjjDHGGFNY5lgl0hhjjDHGGGOSipTI4cOHA9CnT59SfK2VSGOMMcYYY4wxhcXurMYYY4wxxhiTMPr37w/AkCFDgNDffubMmWUbk7ASaYwxxhhjjDEmb1wTaYwxxhhjjDEGXBNpjDHGGGOMMabQ+CLSGGOMMcYYY0ze+CLSGGOMMcYYY0ze+CLSGGOMMcYYY0zeNOoiMoqiNlEUDYui6KMoiiZGUbRxFEULR1E0MoqiT2bfLlSowRpjjDHGGGOMKS+NVSKHAM+kUqmOwJrAROBkYFQqlVoJGDX7vjHGGGOMMcaYJkCDW3xEUdQaGA+skKr2IVEUTQK2SKVS30RR1BYYnUqlVsnxWW7xYYwxxhhjjDHlJa8WH/M04guWB2YAt0ZRtCYwDugPLJFKpb6Z/ZpvgSUa8R1mDqZZs2YA3HjjjQDsv//+Gc9HUQSAYhiXXnopAIMGDQLgf//7X0nGaUyls/516c8AACAASURBVN56VeeKxRZbDIDDDjsMgBNOOAGAzz77rDwDM8YkhmWXXRaAPn36ALDaaqsB0K1bt4zX3X333QAce+yxAPzwww+lGmIiaNeuHQAjRowAoHXr1gAss8wyZRuTMcWgMems8wDrANelUqm1gf8SS12drVDWqjJGUdQ3iqK3oih6qxFjMMYYY4wxxhhTQhqTzrok8FoqlVpu9v3/UHUR2QGns5aFlVdemf79+wMhMqiI2Oeffw7A2WefDcBtt91W8vHlS9u2bQE477zzADjwwAPr9f7tt98egJEjRxZ0XCY7G264IQBPPvkkAAsvvDAQVGLx7LPPsv766wMhmv3oo4+WaphFY9NNNwVg8ODBAHTp0gWAgw8+GIBbb721PAPLk8cffxyAHXfcEYB///0XgCuuuAKAgQMHlmdghlVXXRWAPffcE4C+ffsCsNRSS9X6eqlA559/PgAfffRRsYeYlfvvvx+AHj16AGE+UBbJxIkTAbj33nuBcF6aOnVqKYdZcjbaaCMAXnvttTKPJDtzzz03EOaw3XffPZ2xsMgii2S8Vlk///zzDwDNmzcHYOzYsQDsuuuuAPz4449FHnV5WXrppQE4+uijgZDJMW3aNCAouUlG88u8884LBDVZWSpixowZAHz11VfpTBWd003d7LDDDgDssccetT6v9dR8880HwIsvvljr6z744AMA7rrrLqDgin9e6awNViJTqdS3wNQoinSB2BWYAAwHes9+rDfwWEO/wxhjjDHGGGNMsmiwEgkQRdFawE1AM2AycBBVF6YPAMsAXwA9UqnUzByfYyWyEey2224A3HnnnSywwAJ1vvb7778HYIklklequsIKKwBBtZG6k42vv/4aCGqreOihh4AQ/TaFRxHHffbZBwhqyaKLLgrUrFetjd9//x0ISrO2WyXyzDPPALDNNtsA4TibNGkSAJtttll5BpYn2ZTITz/9FIBOnTqVZ2BzMKo3U0ZFfedsqUK9e1fFdKX2lRKpFcpMyIUUrS+++IJtt90WCFk0lYgUx+OPPz7jvhSrOA8++CAAw4YNA+CBBx4o9hCzcskllwBh7AC//fYbEJSPl156CYBPPvkEgC+//BIIGSnrrLMOADfffDMQVK6myoABAwC48MILMx7v168fANddd13Jx5QvBxxwAADXXHMNAC1atMj7vTpf3HHHHUAyFMm11loLCOuK5ZdfHqjKhoKQsVYOTjnlFCBkizQWZRh+/PHHQNW8ofm/ERTdWIdUKjUeqO1Lujbmc40xxhhjjDHGJJNGXUQ2RRQp7N69O1AzYpgtkjh16tSSO28dccQRAAwZMgQINQx1Mf/88wNw3333AfDyyy8DIfpUDtZdd10gqCHZIu4vvPACAKNGjcq4P2bMmGIPcY5HSsJOO+0EwFVXXQWEuols/PTTTwD8/fffALRq1SpdL6N9UcdaJSqRvXr1AoLSOGXKFAC22247AOaZp7Kn2OHDh5d7CHMsOh40H06ePBmAyy+/HKjKPKmOaiTlrCukCrz33nsAfPjhh0UacU2WW245INTUtmzZMuP5/fbbDwg1djpeVlxxxRqKgX5/JaAsGNWEqsZTNZAnnnhixutVAyXVT3PiXnvtBVTV1pW6TlRj/+uvv4Aqp1HNb7nGsvvuuwNBoVS2ycMPPwyEzI2mhuqWxRNPPAHAPffcU47h1IoUxrgSpwywhmQnvv/++wC88cYbjRxdw1HNphRVKen6PbrdfPPNgXDOlppeSlZeeeW8Xvf2228D4Rzw7bffAmHtpMwNze3az37++ed0NkCxaYw7qzHGGGOMMcaYOYxG1UQWbBAlrInMt0ZBtQmKuL3++utAyPlPgquaIrVdu9bMHta4FSFSrVMc5bLr+XI4miqyLmfZOHK8PPPMM4Ggakn5VXRUuCaycLRp0waAcePGAaHOSvWr2hZy4ZNbmBRuuYqpNmqjjTaiffv2ABxyyCFAqLdTZLScbpL5svjiiwMh8qr5Q1FQOWQmHdVTX3/99UD4XZoXOnbsCJSmT+QWW2wBhONc90ePHg3AlltuWa/P0W0uFJmuzQHvrLPOyuszCsm+++4LBKdSzW+ao1Wnmi8HHXQQEJxQk3DuEppflEVz6KGHArD++uunHcb1e6UY6DVJQse/1LuNN94443nViOfLZZddBoR1ytSpU9PnsyRtv1zo/yFFVU7cccUuCah+U/2p6/N/1rlMaxmdJ3feeWcgnB/LibICtD5ac801M56P+xi88sorQFh/6RxeG1oXT58+vXADridyAH7kkUeA3L4MTz31FAC77LJLCUaXic5NUkvlJaG6ddWWqh5cGRy//PILEDLA9BtbtWoFwLvvvgtUrUMKoEQW153VGGOMMcYYY8ycR2UX7NSBIoOK6KnG4NVXXwWqettAqE1IksKYCzlA1hVpl/uToklSe6SwLrjgggDMNVdVHGHJJZcsylgbw3PPPQfUVCBVP3LaaafV+r4kO6BVRxHPNdZYAwgOp1JY5c6oCGm2bSSlRrUr6lHVmKig6ibkDqj+VvHonhSs4447Lq/PrX58SQHSZxW4x1FRkbqjeUYurIq8VwrarlIgNR8oK6AUCqRQjXMczXPFzpqpbT7V3FNfJakxfPHFF0BwwlRdTH0VSJHkHqWqmRYXXXQRUDU3du7cGQjnLKn8UhCkOJQTHf9aP8SRMlVf1F9Qv/3VV19Nr11KuS/OCbRu3RqAa6+9FgiqkLLU5LRdF1KM5Y6v2s8kKJBbb701EBw84wqkUFaNXqf5+M8//yz2EBuMjoV+/fql5w4h93cpcjqm5Oegc1450JpNPbNz8ccff9R5X1k66itZSiryIlITd/WUkfhFoEwGlNap11bCRWI2JGEPGjQIqN1IR2ki5557LhAuTL777ruMz0gS5513HhCkek3AWpjr4lEXXbJFjqe3aRIpR6F0Q1C61tVXXw2Ek1culCozfvx4IFx86f9SiJQSGejEU6VvuOEGAI488shGf4cuuJRGqNTKm266qdGfXSzatm0L1Eype/rpp4FQ6F4JLLbYYul9UOmrohxlDjqx5puGWt/PjVPo7ykUCvxVUlCl0Pz111/pNF79H5T6qnNZEoibuumir1ClFFqvLLPMMuk1jj67nO0/mgJKEVTQeb31qjL3dNHUpUsXILu52EILLZQuwdB6UwYnRx11VJFGnRv9Lo1JF4UKvgsdRzNnVnXgi6dgVwL6P19xxRXpx2Q+oxRj/X7NIzIeqxSxoS7U3k5GmTItLCVOZzXGGGOMMcYYkzcVqUQq+ldbw16lj0i6rmTlMY5sfeMRIylTp5xySjrlacUVV8x4TbxthpqSKkojK+pyoEiYUseEpHmlrZ500klA+D/EkaJVgCarRUFRop49ewJwyy23AEEFUhrbOeecAwQ16J133gHgm2++yXi8rkL3xqJU2rgi9dhjjzX6szt06ADAqaeemvEdF198MRDs35VyniR0XKlxsbZdQ9PWyknr1q35z3/+U+5hpFFmQTZjnLhSqfvZlMb6ctZZZ9WYg5KA1A61TUhCGmexWXTRRdMmE2uvvTYQMlOSkCIoNVBrEGU8FcvMberUqemUSWVwJFmJVJaUDD/ErFmzyjGcDLR+0LaKbzOZk+Rqb3T44YenM76E0v+LeW7ORvx3KWtIKEtGCuQZZ5wBwO23316qIRac6iVNMvPr3bs3EEzRpDDLDFDzSBINuvJF7Ux0LiiHAimsRBpjjDHGGGOMyZuKVCLjNQHVFUlF63QrVJSumoVhw4YBuZvmJglF8VQzKEVLNsGXXXZZupF0tsiE6g633XZbIJm/X8YyivKpIXWlogihIpxSs6TAqeltElRzqb8bbLBBxuMyHRgxYkSDPk8R6Xbt2qWjZ9rO+j+o0Xg8ep0kttpqq4z7Bx98MADTpk0rx3CaJLkUxkIpj3Fk9FSK78oHtVtaa621gJC5oCwSzfXZUG2Uzg9Dhw4FglFPkunWrVuNY+2aa64p02gCWmtceumlGY9vsskmRf9urVmU9SAzs+r1YElB+57WGTJA0fqrnEh5k7IvdFzts88+db5fLSGk5EE4Fo855piCjbO+qDVQtlo/jXHdddct2ZiKhda+8pH4+uuvGTJkCBDmO7WViWdTyWMjiTXnWvvIGDPuYaJ9VqZAMnISyhRT+79SYCXSGGOMMcYYY0zeVKQSWd2xDKqsmPV3HDlUqY4wHsVTi48k1xcIRSl22GGHjMeldJ111lnp5uBx5HSnmp8kKpD6HWrULne0OFJ9tG2TTLNmzXj++eeBoECqtlFRo3hDczUFV+Q57gBYTBRl3W677TIeVyuPhn6e6pvq4pNPPgFgwoQJDfquYqJ2A3J8+/XXX4HSbptCo9rbOR0df7W5tcYdoEuJXATVnkoOfHKzlgr+448/AqFVgSzspWDeeeedQGUokKpfqq706Rx9zz33lGVM1Ym38pAiUorzafw7tF8kUYmMO2y///77QP0zWYpB3KVUyOlTDd6FmtjrfSeffDKQqXDJQ0AO6botZYuk008/vdbHb775ZiA0sNfY4sjVPgnbKBtaI6pDgRTuyZMnc/jhhwOhpYn46KOPgMrIFlL9u/w/6otaf0htLQVWIo0xxhhjjDHG5E1FKpFxXnvttZz1ZIrWqabhsssuA4LTmaJMcnVNIop8xd1H68rDVxRGzcOfe+65Io2u8Sy33HJAdgVSETRFnxSNSjLnnntujfpC9RpU5CyO6kikLKieN97LL4n06tULCI1+47VDtaForXoWJgn1JlUP0vbt2wNBmZ08eXJ5BlYA1ltvPeaaKzOOqCh0EurPio2Ux9ocWeVanQTU9Ftzt5QR1Yp/8MEHQOjdqvlDvUtvu+22ko21saiWrmXLlulzl2rYpLiWg7iDp1TBcmQw6bs1FyUJndviNaKqg0/COUzrB92KfNdGmjOr/xbVG7/11ltAaRVIke139enTJ+M2G1pPyf34wgsvBMrr3B9HGUGrr756xuObbrppjdfq/6C+u+WcP/JF2SUNRZkRY8eOTdfGFrt3tZVIY4wxxhhjjDF50ySUyPoQ7+ek2kjdJlmJlLOqnJcUdaiNJ598EoCjjz4aqFnLkUQUvbv66quBMHbVDko9TvI2ijN9+vS0WqU+RuPHjwdg1KhRQMhjF1K51MNPPbdKEcX9+eefM75L362aFkX34o5ncWqL1mZ7jWpGk1inKxVZNaL6P8g9rZJJpVI1tk9Se6wWg9oUSLmxxuuUy8n3338PBLfqjTbaCIArr7yy1tdLOdh7771LMLrCovln1qxZ6Zp3KauqCS0H8YwK1WmWA2VTJcHpVGguV72g3CN1jpNjZhJQZs8ee+zRoPdrznzvvffYaaedAPj2228znisHOifnOjdnQ2NXVp72rzXWWAMg3YO8UtD/IUlKai7kBxGvxZf3ghR93Vem1P77759xO2TIkHSW5fTp04s6ZiuRxhhjjDHGGGPyJmpo1KKgg4iisg1CiqSu2uP55ElCPXFuvfVWAHbccccar5Er2hFHHAEkowahvii6stBCCwGhn49yu1Xrowj1jBkzgNDnbdKkSaUbbA6q15zl2haKMCtyJvVYvYGKndtenYEDBwJwwQUXZDyerxIp1VyqsnphVq+VkQKpY/Cnn35q7LALhvozyUlX++INN9wAhPqzSmbSpEmssMIKGY/J5Vq/uymiWkjVGlYnyfO/eOONN4Ca/d7U507zYLEj0MXkjjvuSPe901wyfPhwIKiC7733XsnGE5/vpGZlq98vBlKg9d3KRCp2XaZc4asfG/pb87l6/KqHtdhzzz0BePTRR4s6xvogh09tu3hfSDmt6ty99tprZzwvt+OBAwem1x5JQK79cu2tL7179wZq7uv6fyRhXSXHadXua008bty4dKaG+kVqPSXHU6niTRk58O63337pGlf5OTSAcalUar1cL7ISaYwxxhhjjDEmb+a4msg4ldBrsF27dkBQ4OLOVNWRK2YlKpDir7/+AuC7777LeFw5+lIgZ82aBYS+QEmIlMXJZzvIZe/aa68FQnRNEaRSKpBC7pxSqnbbbTcg1LqI3377DYD//ve/QKhbUg9CbSvVykDYRxUpS5ICKRRplwKpHnWqna5kpFRJYYCg8sypCiSUtydkIdCxqePq2GOPLedwGsWxxx5LixYtgDC/S5lUZobcrdVXuBSUQ4EU6nEtiuVzoEwg9SOVN0Hz5s3z/gyds5ZcckkAOnToACSjrk7nLLmxxl1ZH3roISCc84Qc/gcMGFDsITYIrQ91W1+kRCYZ+Umss846QHBrffrpp1lsscUAmG+++YBQNzgnKJBCv72UWIk0xhhjjDHGGJM3TUKJXHrppevt7Kh8aSkLSXSGlFKg/O9VV10143nVikg1WXDBBdP5/XLu+/zzz0sx1IKiiOcSSywBwCGHHAKE3Hbx1VdfAaFPWjl4/PHHgaByqPZDfeZmzJiRdl/9888/gaBu6fdcfPHFAOlI2umnnw7AvffeW+zhZ0XKoqL9ut1rr70yXic3Md2KBRZYAICTTjoJyKyn0T6qnlpJQrWQcgRWfchTTz0FhG1YyUjJkeINoc9ZU6Y2N1YIx6qcWZPKeutVlacoO0CKSrxvoJTmSmbmzJnp2i7V+e+yyy5A2G+1z6ouTa7lxaRcvRmPO+44Nt54YyCoobl6Y9cX9WmWs3vHjh0B+PXXX4HgTQBhflcvY6F5XjWSymiR666yiVRrr8yHJMyrV111FVDz+FHtbVwJrlSkVinzItu8qG2jeSZJTJs2LeMWSB8fWjcOHjy49AMrE5oTyzH3W4k0xhhjjDHGGJM3TUKJ3HjjjdP1H7l6CEqBVM9B3a+r52KpUQ2kflNcgVRE8NRTTwVCH7Fhw4axyCKLAMFlsVxKZPPmzdO99FZeeeVaXzN27FggqD/rr78+EKIqXbt2rfV9qgVpaJ+nQrLKKqsAITKruh3dzpw5k5kzZwKhhlPvadmyJRBqBNXXLUlOdnGGDRuW1+ukQKpmoTrqB5pE+vbtC4RaTtWpllMVLjSaV3bccUdWXHFFIBx7ynpoSqgGUtkCIok9IevimGOOAUImg36XagZVt7vmmmsCYU5SVkGloho0KY06FqWoxB2GmwJal+hYPf7442u4shaaww47DAgKpOZ6rTOq1zNKvTrjjDOAkLGh9ZfWHepbp/lUWUW6Peqoo4DQG7kc7LrrrkA4/7Zp0wYI9XfdunUDklsvLpdm9XTWekMKvdYbQhlPyraJM27cOCAo0knM0ssH1UQ2ZTQHytFf2/bPP/9Mb8diYyXy/9u78zApynP94983amLQRMEo4hL3xC2KK3hcohAUNIG4a2JE4pK4RSVRQU2iRxSNif4kKp7gAq64ROMS9y0ejxuKgkJEEVBx4xijieLBaOr3R/fdNVMzxfTMdFdVN/fnuria6enpeadr6er3eZ/nMTMzMzMzs6o1RSTy9ddf77ASnXrRKadL+RYjR44E6t9rqRqarTvvvPOAtlVYlZd1yCGHAHFEUtXUpk+fXon8aHbq0Ucfre+gUyy11FIMGDAAaN0bsKWuzqhefvnlQDEqvel1VgU75S0pirr00ktXqibqVhFJ5fqokmkRq5R21mmnnQbEeZ2qTvv5558DMHr06Db5k0WivCtVCD722GPzHE5dRVFU2T6KLGgmUxUK8zp/1IIij2kRyEapxqpzSnt9gVtSpFLRZEV3FA1qVOrXd9BBB7W6X+eULCKtyuXTdYNW0ej6oav5iboukeT1iYwcObJSHbRelO+pvEZFtvU+u9pqq1VWmChHXudJ1WDQrWhVjargK6r5yiuvAPF7eZaUx6kIvnr/KgL53HPPAfFKJ9VeKKK99967Up1YeaiKmM6dOxfofCVh1WjQe0CjSNbMyKOqfWdtu+22QHweUd6tegGn0XWn+nnreeTSSy/l/vvvr+lY0zgSaWZmZmZmZlVrikjkk08+WZkp1AxhcuZQlZu0vls5g0Va7/3b3/4WgE022aTV/aoAqqqWyWpZ+tv0N0E8e52XhQsXtunz2F1ao//SSy+1ul/VuNRXLMs8UFXIHTt2bGa/s4g0g66ZMUW4lCvz1FNPAXEuctEoKrXjjjsCxViZkAdF1F9++WWgMSORaf0gGy0CKYritOzr2Z7ll18eiHMmNVvdiFZYYYVKrvxVV10FwG677QbE55aLLroIgKuvvrru41HEUPn4yUqpuo5Ii0gmI4tpdN2iVTrJ58+CztlaIaPXfb/99qvsW6JexoowplEVzaOOOqqmY+0K7S/JyL5yIBWBLNK1YZrhw4dXqucqktqnT59Wtx3RPqeVbuoZ3EhOPfVUDjzwwFb3aXsWmSKQOr8o/1bRb20bUWX1tdZaC2jbu1W5kVoxkAVHIs3MzMzMzKxqTRGJBCr5AlrXr7XFmk3S1x1Vb82D1rKvs8467X5fkbYjjzwSiKtuKf9MuXaq9glxb6NGpllfbbvx48cDbde61zriaR1TVbBRo0YBcQRS+bmi/EfNsBWVxq+/K4uec0U0btw4AKZNm5bzSLquo36QjUZVFBXF176qaox6/xg+fDgQz1LPmjUr03F2hVbMqJq17LXXXm16Muq9XNV0J06cWPfxJWm1TzKXsSMdVbUuwsoHvb9qVYlqMrSszfDOO+8AcMoppwBxlLgR9O/fH4Cddtqp1f06TpJRoEYwdOjQSkVxRYU7op6d6qOoVUJTp06twwjrS/m766+/fmWlxpQpU/IcUqckI4m6flLF6Wojio899hgQH5fKVc5CtyKRIYQTQggzQggvhhCuDyEsG0JYJ4TwVAhhdgjhhhDCFzt+JjMzMzMzM2sEQevfO/2DIawOPAZsHEXRJyGEG4G7gN2BW6IomhxCuBSYFkXR+A6eq2uDaBLq1TZjxgwg7puYRvlK6r+oSFzv3r0r69oPO+ywVt/Lw/rrrw9QqRLVMmcT4twW5YImaaa5EWbUlzSqhpy27dT78uCDDwbg1ltvzWZgXaSKfIrgjxgxAoj30WY0a9asyoyn+r01cm6vIlTJSKRyIJUT2ah0PlWUXBHHG264AYB+/fq1uv/CCy8Eirn6RtTLrG/fvm2+p2sTRSBVfdHvB/WlyNbee+8NxDUG5s2bV3lPLmrPxGqoKrqq3CvaU9R8fUunPPAPP/ywUp9CqwQaYTWRrouGDRvWqZ+bM2cOEF9/TZgwAYgrVtfIs1EUdVhcpbs5kUsDXw4hLA30AN4GBgBauzEJKPY6NjMzMzMzM6talyORACGE44CzgE+A+4DjgCejKFq//P01gbujKNo0/VkciZQddtgBiPszKYKnmXTNML/11lsAnH322UBcvbVXr1688MILQDyLa1ZLvXr1AuIqyIqOJCkqNGbMmEzG1V2KRCq3oJn7QzabtGqszRKBTNLKldGjRwPxuV4RowULFgDx7HSRffOb3wTi3OpFixYBpeij3ueuvPLKfAZnZoWmfN1p06ZVroOLXn+hJfUTT+uZrtWG6tig3p2qXVDnntv1jUSGEHoCw4B1gNWA5YDBi/2h1j9/RAjhmRDCM10dg5mZmZmZmWWrOzmR+wKDoyg6tPz1wcB2wL7AqlEUfRZC2A44PYqi3Tp4LofNzMysaopAKgdSX6sKq6LhZmbWfIYMGQKU+iOeeeaZgM/7NVT3nMjXgf4hhB6hVGd3IDATeBjYp/yY4cBt3fgdZmZmZmZmViDdzYk8A9gf+Ax4DjgMWB2YDPQq33dQFEWLOngeRyLNzKxqyoF0BNLMzKymqopELt2d3xBF0a+BZGfnOcC23XleMzMzMzMzK6ZuRSJrNghHIs3MrAqKPOpWHIE0MzOriaoikf4QaWZmZmZmZpBBYR0zMzMzMzNbwvhDpJmZmZmZmVXNHyLNzMzMzMysat2qzmpmtbPMMssAcNZZZwHw85//HIADDzwQgBtvvDGfgZmZmZmZteBIpJmZmZmZmVXNkUizglDkceTIkQCocvKCBQtyG5OZmZmZWZIjkWZmZmZmZlY1RyItMyuuuCIAd999NwD9+vVr9f3nn38egMmTJ7f78//6178AuOCCC+o1xFzsuuuuAAwYMKDV/U888QQQvy5mtXTvvffyne98B3DebTM46KCDAJg0aRIAr7zyCgAnn3wyALfddls+A1uMFVZYAYA999wTgKFDhwLwySefsMYaawAwffp0AJ566ikArrnmmqyHaSm+/OUvA/CLX/wCgNNOOw2Agw8+GID77rsPgL///e85jG7xjjnmGABGjBgBwBZbbNHu40IIAEydOhWA//zP/wRK58//+7//q/cwzdpYc801Adhuu+2A+Fp63333BeCmm26qrGyrN0cizczMzMzMrGpLTCRSlS8POeQQII767L///p16Hs2Y/vnPfwbgs88+q9EIa2vppUubVjODv/zlL4E4iqeZwyyttNJKAGy77bZAnPMnm2++eavbNNtssw0A999/f6v777zzTgD+93//t/uDzYBmjRR51evx9ttvA/CDH/wAgA8++CCH0XXOhhtuCMTbRFGEc845B4DTTz8dgEWLFmU/OGtXFEWVfU7nB0ciG5ciKdqmiuDNmzcvryGl+slPfgLE0aCNNtqozWMUAdp+++0B+OEPfwjAI488AsD8+fPrPcxOW3XVVQF48cUXAbj88svbfdxFF10EwMcffwzA+++/n8Hoak/XF6NHj251v/bFo48+GoAjjjgCgJdeeinD0bWmqPef/vQnAHbYYQcAXn/9daAUWQT4y1/+AsA777wDxNeIun655ZZbAHj88cfZcccdsxi6LWH69+8PxPUxdK1YrZEjR1ZWbtT7Pd2RSDMzMzMzM6taSEaDchlECDUfxNprrw3AT3/6UwD2228/ANZaa62aPP+YMWMA+PWvf12T56s1rZmeO3duq/uVJ9PezG+9ffGLXwTiWctf/epXNX3+m2++gCnRNgAAH+1JREFUGeh8dDlrmlW69tprAVhqqaWAOIKw9957A8XMYUozfvx4IJ5xTvr6178OwJtvvpnZmGzxDj/88Mp2E+VG3nTTTXkMybrgG9/4BgAPP/wwAL179wbiSMuTTz6Zz8DaoRy6jz76CGi7GqUlrcBQLr3885//BGDnnXcGYNq0abUeZpcpEvnWW29V9XitZFI+q/Lu9PMLFy6s9RBrQlE9RYOXW245IN7XevXqBcT7pt7zzz333EzHCfFY9X6q6KHGpKhwR6+1VrNdccUVAGy55ZZsvfXWQCmH1/KlfVDHVEernnQu0upEXY+ussoqAKy++upAHJGuJ0UeFTXU9XsavT+/8cYbQJwvfvzxxwOlXEldB3cjEvlsFEVbd/QgRyLNzMzMzMysak2XE9mnTx8gzs1ad9118xxObtRbULPTu+yyCwDPPPNMbmP69NNPATjrrLMAePDBBwEYO3YsEEfkRHl2mknsyPrrrw9Az549geJUhNMMpirV7bTTTq2+r201cOBAAGbOnJnh6AziWczzzjsPiFcw7LXXXkCcR9NMXnvttTb3DR48GHAkspHsscceQBwFK8LqojTXX399u/er8rb2u9tvv71yvlSEQLPsX/nKV4B4hl31DYqwykHRU+XVffvb317s41W7IFmRXKtUjjzySCCO3BaFXnOdN6Vv375A20isosZ5RCKV7639ZrfddgPibaR9ryN63IQJE4BSfYlqf7ZWVE1bUdX333+/8vcs6dcNEydOBOJVT9ruOo8kqXqp6jWIoun/+Mc/6jDK9in3MS0Cqe9rtZ0ikEm//e1v6zC6xXMk0szMzMzMzKrWdJFIrYNW3kS11O9HUaNkVCxJVdXqSTNnmmlO65/YHr0ODzzwABBHIr/3ve/VcohdojXrjz32GEBqhTPl9Ky88sqt7tcsqKJFX/hCaS5Es6C33347UJrBViQ2L8sttxyXXHIJEP+dyUiBI5D5UdRaM7va57SNfv/73wNxVPy9994D4gjDXXfdBcTbTtFvVfxL0jG97rrrVn4m69nsxdlnn30A+N3vfgd4n2wE2hd1Hvz3v/+d53Dapf1JPXGTFMFTf8GW1Jdvq622AuLz6HrrrQfA008/DcCwYcOAfFfb6DpCEURVJ1XOUjJaqr9lk002AeJ8eVWiveOOO4DiVU3Wa63z3bhx4wA49dRTgbYrwDbddNMMR9eazrmKPKm6b1epavq//vWvzKvzq1KxKhf36dOHe+65B4Arr7wSiHM8G6VKfS0MHTq0sopGuY5pqxC/9KUvAfF1lyjar5VxWeYjqyd48utqe6Lr/KpI5htvvJHZOcORSDMzMzMzM6ta00Ui1W/p+eefB9r2HNRM4bvvvgvEfZx69OgBwFFHHQXAV7/61XafX+ukq50h6ArNLGt2QT2X1O+rM9X2NHurmSvNyjUCRSqTFP3RrPXyyy/f6vv/8R//AdSuEm9XqDrwoYceWomcinJBtX7d0Z7sqQKuqisrEqDjRJHI1VZbDYjzeEVRH81Ki3KBtE2Tz6deY1tssUXluXUuytqnn37K559/DsS5WTrvbbnllkAx9k291sn8q2qpKrRm7DVDfeuttwLw4x//uM3PqKrk7Nmzu/Q7s6B9qV+/fkAcgdQqnM6uxqkH7Vcbb7wxEEcBdFzI2Wefnfoces/VMatcNuXAqw6Ceh8fcMABNRl7d6gf4rHHHrvYx2mljFY8JP3Xf/0XUIpMpOVBZWmzzTYD4m0xY8YMAM444wwgrjKpPD1RRdM8qCdpd51wwglAfL649NJLa/K8naHXWcf6qFGjKu8j6tk5ZMgQIH5ve/bZZ1s9h66RVUFXVGFWkbw0H374IVCMns/f//73AZg0aVKbcadtn1NOOQVouwJOK9vUbzxL+jzR2c8V2ieVMylZ9oF3JNLMzMzMzMyq1nSRSDnxxBMBePnll4G4F4yqg2n9uKJVquCUFoEUzfLUM49JUQBFILtTbU+zUKoyWSSqNJasViqTJk0C4NVXXwXi3FDlNiQjkKJcgOeee652g62SohxnnnkmUJoV1/Z7++23AfjjH/8IxBWELVs9e/ZsE4GUtGNNs5Oa9U1G6BRt1vd1m4xEFskjjzxS+Tu+9a1vAcXMp9MxNWvWrC79fNo2GDFiRLv3NwpFqZI547fccgsQR4nylMxPSr7Wyk+rJqqjCIr21RdeeAGIo5zq66ZoZxEiJR257rrrgPRaBcrB1gqqvF188cVAvCogmXc3fPhwoG1vPeV2NgKdLxThV57qz372MyBeCZZltCdJ1xf3339/ZRx77rknEF8/pr3muu4YNGhQq/t1naVc47TzpqLKaT2hs6C/VWNpuUolrUKpzg+HHXZYq/u1IkUr3BqB+t6ff/75re5Xdessc6gdiTQzMzMzM7OqNW0k8m9/+xsA55xzTqtbUW6gZh+UX5Gk2U/N9uRZ+a0zlOM5dOjQdr+vnB9FarOkaK9e+7RKuCeffDIQz4TpccqRSqOeftOmTev+YDvp6quvBuJZTIhnazXbrHxdy8fYsWPbRCBFVZdVEfKqq64C4vOJohzJ6syq8qoKwYoOaYb0sssua/X4mTNnZlLhuauU83TNNdfkPJLuU0ROOeaifHlFGCBekaLerUWmXEBRdcELL7wwj+G0S3lXim6IqheremlXVvao6rWqUW633XYAHHfccQD85je/6cKI60vvYcodVEQh+Z6mqp8/+tGPgOJW2rzhhhtafa0quw899BAQr9BQtLhI10+KkqvSrM55vXv3BtpWQp06dSoQR7LU9zpPTz75ZKWitnqL7r///ov9GVVITkYYk8doGuVO5kERSNUyaVnjQ8e7+kOK3rOVT6rtO336dKBUtwKy7QtZLZ0nFA3XOU63SVqllyVHIs3MzMzMzKxqTRuJVCRO/d1EuRmqapT8vijyoDXHys9rFMnKfUnKz8uDZp1VwS4tKqTKftVSXk0eszGaIdPMkcybN6/qGb40ysPVrK5mEF977TUA/vCHPwD59hvULKiqpa2yyiqtvn/88ccDca5yHlZddVWglMuh2eVHH30UKPUUhbjyY5q0vmDql5bsS6poiX6fIv8DBgyoRI6KKM/KxkmqXtvRDHuam2++udXXmplWZV1FhO+5555Knk8RZ6VFlZ91q31LPQjzWIGRRpUQkxQ10HtAV4wfP77V71BkVis+ihCJVGVgRWQVDT/wwAPbffwrr7wCxNEineOLYLPNNqtcT6hCZ/LYUnROkWatzNFKLq3syJOivopgq15GR1Sj4n/+538AmDBhQmV7Knc1T+otqlvlfC+77LKtHqfq/8nrLlUW10ou7YPJ1Wz//d//XcthL5Yijcq1VS5ossvAueeey+mnnw5QqTiunu86T6iav6Ll6idexHO9Vigov1P9Hzui42677bbrVBeH7nAk0szMzMzMzKrWtJFIRRinTJnSqZ/TjJLWx6f1KsyCZsyUR9CVaqPJflzqQ5NnDzGtqVcvKfW/7GzkUVSlb8yYMUA+lezUiyqZZ6Dcus7QLJsqiOq5FV1P/g5Fz1588cVO/65a0XGiWb1kJPK73/0ukG8kUrPn48aNqxxTf/7zn4E4klgrmt1W7ynNEKo6dFFznIpI56pk1KOrNKs7cOBAAI455hggnrEuKkVQTzrpJCDO7dH5YMKECfkMrB2qMZDs2ahzVDKXrjtUVVKRyLT6BllSJUhtk8GDBy/28VqhoMcVKQIpQ4YMqbxH33vvvUD6e616KSrfLNkrOU/aNlo9pGi4ahXMmTMHiK8ddQ219dZbA/HqrpNOOqlyfldUU9dXqqWRJ3UgSKr2PKfouSL7qvTf0WqdWtCKrlGjRgHx9YPMnz8fiFeTTJkypc1KLOVN6jG6NtEqgCJXx1eeazIC+cQTTwBt+0JqxaRyJffdd19HIs3MzMzMzKx4mjYS2VUHHXQQkG8EUtSzTWvxNdtw3nnnAXFeo74PsNVWWwHxjGYyalWknmga/+677w7AaaedBqT36tSMoKoqivqGqfpWlv2LlGOr3kyimeRqcmkVYdDsk/IHW25X6z5FwPX61oMq9/3+978H4hwhzcg/+OCDdfvdXaFzysSJE4F49UNyBUMzUcVLmTt3bk4j6RxF95PnN0WxJk+enPmY0ujclezlq3yqWkZqFDHRPltNz8l6W2GFFYA4n13vu8nee1qZoJ6fWpVTJKpi2rLWQFd7thbBG2+8AcT7TbVuu+22Vl8PHDiw0jfz1FNPBeKVJ1oJp9oajSiZz6xVO1msolG0MBmBFK1O0fXuVlttVYk8qhKwckN1rGn8ikAqIq3nUI5kEehY0+obRSC17ybp+4pEjhw5MrPaIB1GIkMIV4QQFoQQXmxxX68Qwv0hhFfKtz3L94cQwrgQwuwQwvQQwpbpz2xmZmZmZmaNpppI5ETgIqBlWa1RwINRFJ0TQhhV/vpkYAiwQflfP2B8+bbw1NeoCBFIzSirMpMoN07VslS1UL3pWkrOeBaZojMdRWnUY0szzXvttRcAK664IhC/Ltdddx0AjzzySM3HmqT8xeSsZmfyt9TX7Oyzz+7U79bMm3KCLD+KQKpHn6oyKko+c+bMfAZWJZ0ntPqhEc4bnaWIyg477ADEVQgff/zx3MbUGcnVDqIZ9jwrbqdJ7kf1OFepyqR+l1aH5CkZBZHk16oKnWdl7Y5odcLXvva1yn3V1h3Ydttt6zKmInjwwQcr5xLVLzjqqKOAuE7Daqutls/gukEROlV11T6rHrpZUNVb9ZFN2mijjYA4BxXgrLPOAtJra+h6UdVbtQ+riq1yYlXHQufVPCjimBZ5THrqqafqOZzF6jASGUXRo0By7ckwQOv0JgHfb3H/VVHJk8CKIYQ+mJmZmZmZWVPoak5k7yiKNO35DtC7/P/VgZYfneeX72szRRpCOALILnktxWWXXQbEeYbKm8qTZpQ1q6V196IqdJrl6kzUQNGrRqQZpMMPPxyIq4Xp9Vl55ZWBOB9NUaB6UsW2rrjiiiuAUiWtzlCe5bHHHgvEeXeWneWWWw6IIwk/+9nPgDgCqRzIokcgda7RPqT83Ga00047AXFEb9CgQUAx+4S1pNUOt956K9D2fJ9l37buqmUV5LQol/rb5enuu+8G4ryytJxIvUdNnTo16yF2i66b0igndJdddsliOLlRNX/Vc1Av4p133jmvIXXb9ddf3+prRSBV0TwLqmauvtM6TtZYYw0grl2i99tlllmm8p4siqBrdY0iq8mceFEuZc+ePWvzR2RIr1ceul1YJ4qiKITQ6bVPURT9AfgDQFd+3szMzMzMzLLX1Q+R74YQ+kRR9HZ5ueqC8v1vAi0bm6xRvq+wlDc3e/bsfAfSDlVsu+OOOwC45ZZbgDgSqb6RqoQ3efJk3nnnHSBeUy7Tpk0D4M03s9scRx55JBCvLa/1bEla7ylFJDfffPPK310v6sWjnBbNjP3iF78ASpFt5XCutdZaQDxrqaq0yRmzpIULFwJw0003AXGUy7Kn2U6tXFA1PuVTqEqfIhFF98ADDwDwwQcfAHHvwWak423BgtLbVZYz692hfFtFr3Sryo9Z9QOrBa3cqKZqdUdUVbGIVH22o5xIVYpUz1KdR4pElTIhrmTcUU7k/vvvD8Q9O7O87siDehArv7oRI5Ea8/bbbw/E1yW6fvvss88yH9Odd97Z6laUK6nzyUYbbcS4ceOAeOWGVpjoulkr19Kod20j9nDO8/jqap/I24Hh5f8PB25rcf/B5Sqt/YEPWyx7NTMzMzMzswbXYSQyhHA9sDPwtRDCfODXwDnAjSGEQ4HXgP3KD78L2B2YDSwERtRikMr9UiRHUZ7F0UzgW2+9BaRXyRo/fjwAU6ZMAYoZkdTf0L9//6p/RpVdNfOpXmLqr1Nvc+bMYc01S0FpbT/1/JkzZ063nlsz0Gk9hFSdS9UY62nGjBlAXI215awtwC9/+ctK5DGpo4qY2mbqC1eEysFLKuVJqFeYqvIpuq785UaKClljUFQnSdW6FVltBJtuumnNnit5ri2Sv/71rwCMHj0agLFjx7b7OJ1XFP0pYiSyZVVWVZ9Ou45QBfXke3MRendm4cUXS53wlH+nSGwRryuThg0bBsTXIVqd8vDDD+c2po5oNcmyyy7bpirrOeecA6Qfe3lIrhCsVS/H5PNmqcMPkVEUpZ2pB7bz2AhovyZvN2h5olohyOI+TKrppto/pNGBUoSCOrWQ9saadSPquXPnVj70f/Ob3wTiC3CdrDr7YVIfHu+9914gvQiIlghleUGvBvZaatyV5SwfffQREO/XWl6R1Qd/a0sXUNoWyYu9PfbYAyhme4XOUMEPLWHS181Ay5s0mdVRUZCi0IW40hWSlLpQRFp6v99+pflllddXcSMVCVJBqmpK2esiUUU1ksvu9F6uEv15UoGVjlpcXHvttQD85S9/qfuYukotH6DjJYEqJPS9730PiCfAtb2b3eDBg4H42kT7QZE/RA4ZMgSIJ6VE12lFLg6nSZjTTjutEjSYPn06UKx9TufBkSNHAvDEE08AcWCou9eqyQDT+eef363n64yuLmc1MzMzMzOzJVC3q7NmSTOOWt6nGeWXXnqp8hi1dzjkkEMA6NGjx2KfUy0vmiXxWzNfSVnPWh9wwAGVBqiKSKqEsiKFWoal2ctkYRktQdZstZ4nrQSzktu15CZLKnKhdh2ava2msI/Ku99zzz1A40RKOkORiUahfUwRSEWWNSu72267AY2ZhN+ejpZWNwOdH7Kcpe0OnRcVgVN0OI+VFl2lBuB77rlnq/uHDh0KxDPoV111FRAvT3v11VcrSwHXW289IC6MpFVGyX1WK1SUopIHnff1npX8ux999FEgLpqn80taUbUi2GCDDTp8jP7OUaNGAfG2UYGxPKJZWlKqa0BFqt59912gNoVivvrVrwLQr18/APbee28g3p8bIQVF0XIVBVRql/bVIlJBHR1Hm2++ObfffjsQR/3yKASUJllUUucwRSS1EkPndN2vNKm0lRr6W5U6JllebzkSaWZmZmZmZlULRZhx7qhPpNZmq1SvqIRvy3xGNbnVLFTS/PnzASqzFr/5zW+A6nIyGsEJJ5wAxIm28+bNA+L8kSxzt9T+4MQTTwRg7bXXruvv06xNWiGKPBxyyCGpkUXNmGv29uOPP85sXPUya9YsIC4oIF//+teB4kf8FXFUXpWOG1FURMdVs1Duklp8KA9XbXQarRl6SwcccAAQNz5P5v4Uzbe//W0gnk3u1asXEBeL23LLLYH4vazI1BJHBSRGjCjV2tP5IO3645NPPqlEkDq6RtF7t3K7Wq5Myor2rSuvvBKI/z7RSpV99tkHKHYOZJJadV188cWV11Z/h25PP/10II6onnHGGQCcffbZAHz++eeZjVcOP/xwAM4880wgrq2hSJtWkdx1111A3KZLtRv0t4miSdq2Q4cOZaONNgLiSNDTTz8NxA3ti5wLqfdorWzQ37DZZpsBxcyF1LnwmmuuAWDXXXcFSlFTRf9V3KiItPLixhtvBNpGENMkI4u61lUBTT1PMhe9m56Nomjrjh7kSKSZmZmZmZlVrSEikcoFueCCC4C4yl5nKAKimbFmLTmtSKRmKBS9Va5eHjQDeP311wPxTNdKK63Ured97733gLiUs2anmiVPrRE1aiRSEZNHHnkEKOVYQByBU7XBZt23kpFIvS8ouqPZ6kaklQDKT1befBH16NGj8lorz0q0uuTkk0/OfFy1okj+hAkTgLj6sfLlW9L7fvIaRVVYtT1VI+HVV1+tw4hh+eWXZ5tttgHidgc6nynyqOix6jaIIpDadnqvaiQ6N1ZTJVwRSN3mSfuWIvd9+/YF4hxAndvSVq1J2n4IceRV15XKt1b+dZFpX1bFZEXHtfqkiHTuU661bLjhhoWO+qZRZFLX57qtNkKZpFzLGuXLOxJpZmZmZmZmtdUQ1Vk1A6Q8pc5EIrV2WDNjRVznXQ8LFy4E8m1CKorefOc73wHiHkrJ7XjqqacCcfU0WWWVVYB4tk/GjRsHwKefflrjEduSQlVYlQejCKR6TBUpv7aeVBVTfS91nmyk3K00P/7xj4G2fYaLaNCgQW0ikIqGjxkzJo8h1ZSihYp2qLqlKoCqIu3ChQsrfYRVwXTTTTcFqFT9fu211zIZc48ePbjkkksAWGaZZYBSdBLi96YkHTfKFWzk40g1J3bcccdKLpf6Ieu9WvtmkVZ4aaXSfffd1+pWFGEdOLDU8lz7oKLOaau3dK586KGHePzxxwG44447ajn0ulLdEEVqdX39wgsv5DamaukcIHrfVn2URqOIoW6VO64IpSKLuk3uk8oHV2/xPCp2OxJpZmZmZmZmVWuInMgWjwPi2ctf/epXABx//PGVxygHMBl5LHIfplpSTqReE/VWNMtCMidSOUBaRbBo0aJ8Bpaw1VZbAXFO0yabbALEORfKKbbGp3O/IpFFzom0YtL57IEHHgDaVl/V+WLs2LFAvBKoKOc7M1EP9WTVeEXLFZktEo1ZKwLUZUD5nEWttdDgnBNpZmZmZmZmtdVQkUgzs+7q2bNnJVdafSHVq/O8887La1hWJ45EmpmVvPzyywCsu+66re6/7rrrADj44IMzH1NHlBetyrqDBg0Cit2Hswk4EmlmZmZmZma11RDVWc3MamXs2LGVCKRypsePH5/jiMzMzOpvypQpQByJPProo4G4j3cRLSlV0huRI5FmZmZmZmZWNedEmplZ03JOpJmZWac4J9LMzMzMzMxqqyg5ke8BH5dvLT9fw9sgb94G+fM2yF/NtsEXvuC50i7ycZA/b4Ni8HbIn7dBtqpqMl+I5awAIYRnqgmdWv14G+TP2yB/3gb58zbIn7dB/rwNisHbIX/eBsXkKVozMzMzMzOrmj9EmpmZmZmZWdWK9CHyD3kPwLwNCsDbIH/eBvnzNsift0H+vA2Kwdshf94GBVSYnEgzMzMzMzMrviJFIs3MzMzMzKzgCvEhMoQwOIQwK4QwO4QwKu/xLClCCPNCCC+EEJ4PITxTvq9XCOH+EMIr5dueeY+zmYQQrgghLAghvNjivnZf81AyrnxcTA8hbJnfyJtHyjY4PYTwZvlYeD6EsHuL740ub4NZIYTd8hl1cwkhrBlCeDiEMDOEMCOEcFz5fh8LGVnMNvCxkJEQwrIhhKdDCNPK2+CM8v3rhBCeKr/WN4QQvli+/0vlr2eXv792nuNvBovZBhNDCHNbHAd9y/f7XFQnIYSlQgjPhRDuLH/t46Dgcv8QGUJYCrgYGAJsDBwYQtg431EtUXaJoqhvi9LJo4AHoyjaAHiw/LXVzkRgcOK+tNd8CLBB+d8RwPiMxtjsJtJ2GwBcUD4W+kZRdBdA+Vx0ALBJ+WcuKZ+zrHs+A34eRdHGQH/g6PJr7WMhO2nbAHwsZGURMCCKos2BvsDgEEJ/4FxK22B94O/AoeXHHwr8vXz/BeXHWfekbQOAE1scB8+X7/O5qH6OA/7a4msfBwWX+4dIYFtgdhRFc6Io+hSYDAzLeUxLsmHApPL/JwHfz3EsTSeKokeB9xN3p73mw4CropIngRVDCH2yGWnzStkGaYYBk6MoWhRF0VxgNqVzlnVDFEVvR1E0tfz/f1K6cFgdHwuZWcw2SONjocbK+/NH5S+XKf+LgAHAzeX7k8eBjo+bgYEhhJDRcJvSYrZBGp+L6iCEsAawB3BZ+euAj4PCK8KHyNWBN1p8PZ/Fv5FZ7UTAfSGEZ0MIR5Tv6x1F0dvl/78D9M5naEuUtNfcx0a2jikvT7oixMu4vQ3qrLwUaQvgKXws5CKxDcDHQmbKS/ieBxYA9wOvAh9EUfRZ+SEtX+fKNih//0NgpWxH3HyS2yCKIh0HZ5WPgwtCCF8q3+fjoD7+H3AS8O/y1yvh46DwivAh0vKzQxRFW1JannF0CGGnlt+MSqV7Xb43Q37NczMeWI/Scqa3gd/lO5wlQwhheeCPwPFRFP2j5fd8LGSjnW3gYyFDURR9HkVRX2ANSpHdDXMe0hInuQ1CCJsCoylti22AXsDJOQ6xqYUQvgssiKLo2bzHYp1ThA+RbwJrtvh6jfJ9VmdRFL1Zvl0A3ErpDexdLc0o3y7Ib4RLjLTX3MdGRqIoerd8IfFvYALxMj1vgzoJISxD6cPLtVEU3VK+28dChtrbBj4W8hFF0QfAw8B2lJZILl3+VsvXubINyt9fAfhbxkNtWi22weDycu8oiqJFwJX4OKin7YGhIYR5lFLaBgAX4uOg8IrwIXIKsEG5CtMXKSXu357zmJpeCGG5EMJX9H9gV+BFSq/98PLDhgO35TPCJUraa347cHC5Glx/4MMWS/2shhI5LXtSOhagtA0OKFeDW4dSMYWnsx5fsynnr1wO/DWKovNbfMvHQkbStoGPheyEEFYOIaxY/v+XgUGUclMfBvYpPyx5HOj42Ad4KHKz725J2QYvtZjMCpRy8VoeBz4X1VAURaOjKFojiqK1KX0GeCiKoh/i46Dwlu74IfUVRdFnIYRjgHuBpYAroiiakfOwlgS9gVvLuchLA9dFUXRPCGEKcGMI4VDgNWC/HMfYdEII1wM7A18LIcwHfg2cQ/uv+V3A7pQKWCwERmQ+4CaUsg12Lpdwj4B5wE8AoiiaEUK4EZhJqZrl0VEUfZ7HuJvM9sCPgBfKuUgAp+BjIUtp2+BAHwuZ6QNMKle5/QJwYxRFd4YQZgKTQwhjgOcofdinfHt1CGE2peJgB+Qx6CaTtg0eCiGsDATgeeCn5cf7XJSdk/FxUGjBH97NzMzMzMysWkVYzmpmZmZmZmYNwh8izczMzMzMrGr+EGlmZmZmZmZV84dIMzMzMzMzq5o/RJqZmZmZmVnV/CHSzMzMzMzMquYPkWZmZmZmZlY1f4g0MzMzMzOzqv1/+UuE6VuruBQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a4b1518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples = np.concatenate([np.concatenate([x_train[i] for i in [int(random.random() * len(x_train)) for i in range(16)]], axis=1) for i in range(4)], axis=0)\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.imshow(samples, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we need to pre-process the data for Keras.  To do so, we will reshape the image arrays from $n$x28x28 to $n$x784, so each row of the data is the full \"unrolled\" list of pixels, and we will ensure they are float32 for precision. We then normalize the pixel values (which are naturally between 0 and 255) so that they are all between 0 and 1 instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape to input vectors\n",
    "x_train = x_train.reshape(60000, 784)\n",
    "x_test = x_test.reshape(10000, 784)\n",
    "\n",
    "# make float32\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "\n",
    "# normalize to (0-1)\n",
    "x_train /= 255\n",
    "x_test /= 255\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In classification, we will eventually structure our neural networks so that they have $n$ output neurons, 1 for each class. The idea is whichever output neuron has the highest value at the end is the predicted class. For this, we must structure our labels as \"one-hot\" vectors, which are vectors of length $n$ where $n$ is the number of classes, and the elements are all 0 except for the correct label, which is 1. For example, an image of the number 3 would be:\n",
    "\n",
    "$[0, 0, 0, 1, 0, 0, 0, 0, 0, 0]$\n",
    "\n",
    "And for the number 7 it would be:\n",
    "\n",
    "$[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]$\n",
    "\n",
    "Notice we are zero-indexed again, so the first element is for the digit 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first sample of y_train before one-hot vectorization 5\n",
      "first sample of y_train after one-hot vectorization [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "print(\"first sample of y_train before one-hot vectorization\", y_train[0])\n",
    "\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "print(\"first sample of y_train after one-hot vectorization\", y_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's make a neural network for MNIST. We'll give it two layers of 100 neurons each, with sigmoid activations. Then we will make the output layer go through a softmax activation, the standard for classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(100, activation='sigmoid', input_dim=784))\n",
    "model.add(Dense(100, activation='sigmoid'))\n",
    "model.add(Dense(num_classes, activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, the network has 784 * 100 = 78,400 weights in the first layer, 100 * 100 = 10,000 weights in the second layer, and 100 * 10 = 1,000 weights in the output layer, plus 100 + 100 + 10 = 210 biases, giving us a total of 78,400 + 10,000 + 1,000 + 210 = 89,610 parameters. We can see this in the summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 100)               78500     \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 89,610\n",
      "Trainable params: 89,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will compile our model to optimize for the categorical cross-entropy loss as described earlier, and we will use SGD as our optimizer again. We will also include the optional argument `metrics` to keep track of the accuracy during training, in addition to just the loss. The accuracy is the % of samples classified correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='sgd',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now train our network for 20 epochs and use a batch size of 100. We will talk in more detail later on how to choose these hyper-parameters. We use our validation set to evaluate our performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 2.2794 - acc: 0.1910 - val_loss: 2.2425 - val_acc: 0.3578\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 2.2101 - acc: 0.3785 - val_loss: 2.1680 - val_acc: 0.5047\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 2.1182 - acc: 0.5069 - val_loss: 2.0495 - val_acc: 0.5663\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 1.9693 - acc: 0.5674 - val_loss: 1.8622 - val_acc: 0.6098\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 1.7526 - acc: 0.6198 - val_loss: 1.6146 - val_acc: 0.6593\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 1.5026 - acc: 0.6680 - val_loss: 1.3660 - val_acc: 0.7114\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 1.2761 - acc: 0.7147 - val_loss: 1.1638 - val_acc: 0.7313\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 1.1001 - acc: 0.7481 - val_loss: 1.0126 - val_acc: 0.7680\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.9690 - acc: 0.7720 - val_loss: 0.9003 - val_acc: 0.7851\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.8696 - acc: 0.7915 - val_loss: 0.8138 - val_acc: 0.8004\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.7915 - acc: 0.8070 - val_loss: 0.7436 - val_acc: 0.8155\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.7286 - acc: 0.8196 - val_loss: 0.6870 - val_acc: 0.8272\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 2s 26us/step - loss: 0.6769 - acc: 0.8297 - val_loss: 0.6399 - val_acc: 0.8379\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.6340 - acc: 0.8389 - val_loss: 0.6007 - val_acc: 0.8462\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.5980 - acc: 0.8462 - val_loss: 0.5673 - val_acc: 0.8517\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.5674 - acc: 0.8527 - val_loss: 0.5390 - val_acc: 0.8569\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 2s 26us/step - loss: 0.5414 - acc: 0.8586 - val_loss: 0.5152 - val_acc: 0.8632\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.5187 - acc: 0.8633 - val_loss: 0.4938 - val_acc: 0.8681\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 2s 26us/step - loss: 0.4990 - acc: 0.8681 - val_loss: 0.4756 - val_acc: 0.8723\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.4817 - acc: 0.8722 - val_loss: 0.4598 - val_acc: 0.8744\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x12ccfa518>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train,\n",
    "          batch_size=100,\n",
    "          epochs=20,\n",
    "          verbose=1,\n",
    "          validation_data=(x_test, y_test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the performance of the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.45980212097167966\n",
      "Test accuracy: 0.8744\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After 20 epochs, we have an accuracy of around 87%. Perhaps we can train it for a bit longer to get better performance?  Let's run fit again for another 20 epochs. Notice that as long as we don't recompile the model, we can keep running fit to try to improve the model. So we know that we don't necessarily have to decide ahead of time how long to train for, we can keep training as we see fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.4664 - acc: 0.8753 - val_loss: 0.4453 - val_acc: 0.8788\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.4528 - acc: 0.8786 - val_loss: 0.4327 - val_acc: 0.8828\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.4405 - acc: 0.8812 - val_loss: 0.4211 - val_acc: 0.8845\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.4295 - acc: 0.8835 - val_loss: 0.4109 - val_acc: 0.8879\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.4196 - acc: 0.8862 - val_loss: 0.4017 - val_acc: 0.8900\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.4106 - acc: 0.8880 - val_loss: 0.3937 - val_acc: 0.8916\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.4023 - acc: 0.8897 - val_loss: 0.3858 - val_acc: 0.8941\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.3949 - acc: 0.8917 - val_loss: 0.3793 - val_acc: 0.8944\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.3880 - acc: 0.8933 - val_loss: 0.3727 - val_acc: 0.8971\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.3817 - acc: 0.8948 - val_loss: 0.3673 - val_acc: 0.8981\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3759 - acc: 0.8957 - val_loss: 0.3614 - val_acc: 0.8993\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.3705 - acc: 0.8971 - val_loss: 0.3560 - val_acc: 0.9005\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.3654 - acc: 0.8983 - val_loss: 0.3515 - val_acc: 0.9014\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.3606 - acc: 0.8993 - val_loss: 0.3469 - val_acc: 0.9009\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 2s 26us/step - loss: 0.3562 - acc: 0.9007 - val_loss: 0.3431 - val_acc: 0.9027\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.3520 - acc: 0.9016 - val_loss: 0.3393 - val_acc: 0.9027\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.3481 - acc: 0.9025 - val_loss: 0.3354 - val_acc: 0.9047\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.3443 - acc: 0.9035 - val_loss: 0.3324 - val_acc: 0.9041\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.3407 - acc: 0.9040 - val_loss: 0.3289 - val_acc: 0.9055\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.3373 - acc: 0.9046 - val_loss: 0.3259 - val_acc: 0.9069\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11b99fcf8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train,\n",
    "          batch_size=100,\n",
    "          epochs=20,\n",
    "          verbose=1,\n",
    "          validation_data=(x_test, y_test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point our accuracy is at 90%. This seems not too bad! Random guesses would only get us 10% accuracy, so we must be doing something right. But 90% is not acceptable for MNIST. The current record for MNIST has 99.8% accuracy, which means our model makes 500 times as many errors as the best network.\n",
    "\n",
    "So how can we improve it?  What if we make the network bigger? And train for longer?  Let's give it two layers of 256 neurons each, and then train for 60 epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_21 (Dense)             (None, 256)               200960    \n",
      "_________________________________________________________________\n",
      "dense_22 (Dense)             (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 10)                2570      \n",
      "=================================================================\n",
      "Total params: 269,322\n",
      "Trainable params: 269,322\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(256, activation='sigmoid', input_dim=784))\n",
    "model.add(Dense(256, activation='sigmoid'))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='sgd',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The network now has 269,322 parameters, which is more than 3 times as many as the last network. Now train it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/60\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 2.2673 - acc: 0.2190 - val_loss: 2.2256 - val_acc: 0.2695\n",
      "Epoch 2/60\n",
      "60000/60000 [==============================] - 2s 37us/step - loss: 2.1828 - acc: 0.4033 - val_loss: 2.1243 - val_acc: 0.4352\n",
      "Epoch 3/60\n",
      "60000/60000 [==============================] - 2s 40us/step - loss: 2.0538 - acc: 0.5165 - val_loss: 1.9559 - val_acc: 0.5852\n",
      "Epoch 4/60\n",
      "60000/60000 [==============================] - 2s 38us/step - loss: 1.8457 - acc: 0.5946 - val_loss: 1.7022 - val_acc: 0.6786\n",
      "Epoch 5/60\n",
      "60000/60000 [==============================] - 2s 38us/step - loss: 1.5702 - acc: 0.6608 - val_loss: 1.4133 - val_acc: 0.7070\n",
      "Epoch 6/60\n",
      "60000/60000 [==============================] - 2s 37us/step - loss: 1.3021 - acc: 0.7172 - val_loss: 1.1698 - val_acc: 0.7369\n",
      "Epoch 7/60\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 1.0892 - acc: 0.7600 - val_loss: 0.9857 - val_acc: 0.7899\n",
      "Epoch 8/60\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.9319 - acc: 0.7898 - val_loss: 0.8538 - val_acc: 0.8040\n",
      "Epoch 9/60\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.8170 - acc: 0.8108 - val_loss: 0.7549 - val_acc: 0.8228\n",
      "Epoch 10/60\n",
      "60000/60000 [==============================] - 2s 36us/step - loss: 0.7316 - acc: 0.8266 - val_loss: 0.6810 - val_acc: 0.8375\n",
      "Epoch 11/60\n",
      "60000/60000 [==============================] - 2s 36us/step - loss: 0.6667 - acc: 0.8371 - val_loss: 0.6248 - val_acc: 0.8436\n",
      "Epoch 12/60\n",
      "60000/60000 [==============================] - 2s 36us/step - loss: 0.6164 - acc: 0.8460 - val_loss: 0.5800 - val_acc: 0.8554\n",
      "Epoch 13/60\n",
      "60000/60000 [==============================] - 2s 38us/step - loss: 0.5765 - acc: 0.8529 - val_loss: 0.5437 - val_acc: 0.8604\n",
      "Epoch 14/60\n",
      "60000/60000 [==============================] - 2s 36us/step - loss: 0.5444 - acc: 0.8592 - val_loss: 0.5149 - val_acc: 0.8668\n",
      "Epoch 15/60\n",
      "60000/60000 [==============================] - 2s 37us/step - loss: 0.5180 - acc: 0.8645 - val_loss: 0.4904 - val_acc: 0.8707\n",
      "Epoch 16/60\n",
      "60000/60000 [==============================] - 2s 37us/step - loss: 0.4958 - acc: 0.8687 - val_loss: 0.4713 - val_acc: 0.8750\n",
      "Epoch 17/60\n",
      "60000/60000 [==============================] - 2s 40us/step - loss: 0.4770 - acc: 0.8724 - val_loss: 0.4535 - val_acc: 0.8798\n",
      "Epoch 18/60\n",
      "60000/60000 [==============================] - 2s 36us/step - loss: 0.4608 - acc: 0.8766 - val_loss: 0.4381 - val_acc: 0.8820\n",
      "Epoch 19/60\n",
      "60000/60000 [==============================] - 2s 37us/step - loss: 0.4469 - acc: 0.8790 - val_loss: 0.4254 - val_acc: 0.8859\n",
      "Epoch 20/60\n",
      "60000/60000 [==============================] - 2s 37us/step - loss: 0.4346 - acc: 0.8812 - val_loss: 0.4148 - val_acc: 0.8863\n",
      "Epoch 21/60\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.4239 - acc: 0.8843 - val_loss: 0.4040 - val_acc: 0.8891\n",
      "Epoch 22/60\n",
      "60000/60000 [==============================] - 2s 38us/step - loss: 0.4142 - acc: 0.8860 - val_loss: 0.3946 - val_acc: 0.8901\n",
      "Epoch 23/60\n",
      "60000/60000 [==============================] - 2s 37us/step - loss: 0.4055 - acc: 0.8882 - val_loss: 0.3880 - val_acc: 0.8915\n",
      "Epoch 24/60\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.3979 - acc: 0.8898 - val_loss: 0.3800 - val_acc: 0.8938\n",
      "Epoch 25/60\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.3909 - acc: 0.8916 - val_loss: 0.3730 - val_acc: 0.8951\n",
      "Epoch 26/60\n",
      "60000/60000 [==============================] - 2s 38us/step - loss: 0.3844 - acc: 0.8931 - val_loss: 0.3666 - val_acc: 0.8977\n",
      "Epoch 27/60\n",
      "60000/60000 [==============================] - 2s 39us/step - loss: 0.3786 - acc: 0.8942 - val_loss: 0.3616 - val_acc: 0.8983\n",
      "Epoch 28/60\n",
      "60000/60000 [==============================] - 2s 40us/step - loss: 0.3733 - acc: 0.8956 - val_loss: 0.3569 - val_acc: 0.8989\n",
      "Epoch 29/60\n",
      "60000/60000 [==============================] - 3s 42us/step - loss: 0.3683 - acc: 0.8969 - val_loss: 0.3526 - val_acc: 0.9000\n",
      "Epoch 30/60\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.3637 - acc: 0.8972 - val_loss: 0.3480 - val_acc: 0.9012\n",
      "Epoch 31/60\n",
      "60000/60000 [==============================] - 2s 41us/step - loss: 0.3593 - acc: 0.8989 - val_loss: 0.3442 - val_acc: 0.9013\n",
      "Epoch 32/60\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3552 - acc: 0.8997 - val_loss: 0.3403 - val_acc: 0.9023\n",
      "Epoch 33/60\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.3515 - acc: 0.9004 - val_loss: 0.3367 - val_acc: 0.9031\n",
      "Epoch 34/60\n",
      "60000/60000 [==============================] - 3s 46us/step - loss: 0.3478 - acc: 0.9014 - val_loss: 0.3344 - val_acc: 0.9038\n",
      "Epoch 35/60\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3445 - acc: 0.9019 - val_loss: 0.3300 - val_acc: 0.9049\n",
      "Epoch 36/60\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.3413 - acc: 0.9031 - val_loss: 0.3272 - val_acc: 0.9049\n",
      "Epoch 37/60\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.3382 - acc: 0.9039 - val_loss: 0.3247 - val_acc: 0.9059\n",
      "Epoch 38/60\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.3353 - acc: 0.9047 - val_loss: 0.3225 - val_acc: 0.9068\n",
      "Epoch 39/60\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.3325 - acc: 0.9052 - val_loss: 0.3195 - val_acc: 0.9076\n",
      "Epoch 40/60\n",
      "60000/60000 [==============================] - 3s 50us/step - loss: 0.3298 - acc: 0.9061 - val_loss: 0.3174 - val_acc: 0.9088\n",
      "Epoch 41/60\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.3273 - acc: 0.9064 - val_loss: 0.3151 - val_acc: 0.9096\n",
      "Epoch 42/60\n",
      "60000/60000 [==============================] - 3s 47us/step - loss: 0.3248 - acc: 0.9072 - val_loss: 0.3116 - val_acc: 0.9104\n",
      "Epoch 43/60\n",
      "60000/60000 [==============================] - 3s 46us/step - loss: 0.3225 - acc: 0.9074 - val_loss: 0.3099 - val_acc: 0.9107\n",
      "Epoch 44/60\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.3202 - acc: 0.9081 - val_loss: 0.3082 - val_acc: 0.9112\n",
      "Epoch 45/60\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.3180 - acc: 0.9088 - val_loss: 0.3061 - val_acc: 0.9124\n",
      "Epoch 46/60\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3158 - acc: 0.9092 - val_loss: 0.3038 - val_acc: 0.9126\n",
      "Epoch 47/60\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3139 - acc: 0.9099 - val_loss: 0.3024 - val_acc: 0.9127\n",
      "Epoch 48/60\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.3118 - acc: 0.9101 - val_loss: 0.3005 - val_acc: 0.9135\n",
      "Epoch 49/60\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.3098 - acc: 0.9114 - val_loss: 0.2985 - val_acc: 0.9136\n",
      "Epoch 50/60\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.3080 - acc: 0.9116 - val_loss: 0.2969 - val_acc: 0.9135\n",
      "Epoch 51/60\n",
      "60000/60000 [==============================] - 3s 47us/step - loss: 0.3062 - acc: 0.9119 - val_loss: 0.2957 - val_acc: 0.9147\n",
      "Epoch 52/60\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.3044 - acc: 0.9130 - val_loss: 0.2941 - val_acc: 0.9152\n",
      "Epoch 53/60\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.3027 - acc: 0.9135 - val_loss: 0.2928 - val_acc: 0.9152\n",
      "Epoch 54/60\n",
      "60000/60000 [==============================] - 3s 49us/step - loss: 0.3010 - acc: 0.9137 - val_loss: 0.2916 - val_acc: 0.9163\n",
      "Epoch 55/60\n",
      "60000/60000 [==============================] - 3s 56us/step - loss: 0.2994 - acc: 0.9140 - val_loss: 0.2890 - val_acc: 0.9157\n",
      "Epoch 56/60\n",
      "60000/60000 [==============================] - 4s 61us/step - loss: 0.2978 - acc: 0.9147 - val_loss: 0.2879 - val_acc: 0.9158\n",
      "Epoch 57/60\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.2962 - acc: 0.9150 - val_loss: 0.2864 - val_acc: 0.9176\n",
      "Epoch 58/60\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.2948 - acc: 0.9155 - val_loss: 0.2853 - val_acc: 0.9175\n",
      "Epoch 59/60\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.2933 - acc: 0.9158 - val_loss: 0.2838 - val_acc: 0.9178\n",
      "Epoch 60/60\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.2916 - acc: 0.9161 - val_loss: 0.2824 - val_acc: 0.9182\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11be5c8d0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train,\n",
    "          batch_size=100,\n",
    "          epochs=60,\n",
    "          verbose=1,\n",
    "          validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surprisingly, this new network only achieves 91.6% accuracy, which is only a bit better than the last one.\n",
    "\n",
    "So maybe bigger is not better! The problem is that just making the network bigger has diminishing improvements for us. We are going to need to make more improvements to get good results. We will introduce some improvements in the next notebook.\n",
    "\n",
    "Before we do that,let's try what we have so far with [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html). CIFAR-10 is a dataset which contains 60,000 32x32x3 RGB-color images of airplanes, automobiles, birds, cats, deer, dogs, frogs, horses, ships, and trucks.\n",
    "\n",
    "The next cell will import that dataset, and tell us about it's shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50000 train samples, 10000 test samples\n",
      "training data shape:  (50000, 32, 32, 3) (50000, 1)\n",
      "test data shape:  (10000, 32, 32, 3) (10000, 1)\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import cifar10\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "num_classes = 10\n",
    "\n",
    "print('%d train samples, %d test samples'%(x_train.shape[0], x_test.shape[0]))\n",
    "print(\"training data shape: \", x_train.shape, y_train.shape)\n",
    "print(\"test data shape: \", x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a random sample of images from CIFAR-10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1256a9780>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125628470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples = np.concatenate([np.concatenate([x_train[i] for i in [int(random.random() * len(x_train)) for i in range(16)]], axis=1) for i in range(6)], axis=0)\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.imshow(samples, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with MNIST, we need to pre-process the data by converting to float32 precision, reshaping so each row is a single input vector, and normalizing between 0 and 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape to input vectors\n",
    "x_train = x_train.reshape(50000, 32*32*3)\n",
    "x_test = x_test.reshape(10000, 32*32*3)\n",
    "\n",
    "# make float32\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "\n",
    "# normalize to (0-1)\n",
    "x_train /= 255\n",
    "x_test /= 255\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert labels to one-hot vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's copy the last network we used for MNIST, and see how this architecture does for CIFAR-10. Note that the `input_dim` of the first layer is no longer 784 as it was for MNIST, but now it is 32x32x3=3072. This means we will have more parameters in this network than the MNIST network of the same architecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_33 (Dense)             (None, 100)               307300    \n",
      "_________________________________________________________________\n",
      "dense_34 (Dense)             (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dense_35 (Dense)             (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 318,410\n",
      "Trainable params: 318,410\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(100, activation='sigmoid', input_dim=3072))\n",
    "model.add(Dense(100, activation='sigmoid'))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This network now has 318,410 parameters, compared to 269,322 as we had in the equivalent MNIST network. Let's compile it to learn with SGD and the same categorical cross-entropy loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='sgd',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now train for 60 epochs, same batch size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/60\n",
      "50000/50000 [==============================] - 2s 44us/step - loss: 2.2953 - acc: 0.1635 - val_loss: 2.2710 - val_acc: 0.2044\n",
      "Epoch 2/60\n",
      "50000/50000 [==============================] - 2s 39us/step - loss: 2.2556 - acc: 0.2231 - val_loss: 2.2391 - val_acc: 0.2233\n",
      "Epoch 3/60\n",
      "50000/50000 [==============================] - 2s 41us/step - loss: 2.2188 - acc: 0.2426 - val_loss: 2.1973 - val_acc: 0.2510\n",
      "Epoch 4/60\n",
      "50000/50000 [==============================] - 2s 40us/step - loss: 2.1728 - acc: 0.2514 - val_loss: 2.1499 - val_acc: 0.2524\n",
      "Epoch 5/60\n",
      "50000/50000 [==============================] - 2s 39us/step - loss: 2.1261 - acc: 0.2618 - val_loss: 2.1060 - val_acc: 0.2519\n",
      "Epoch 6/60\n",
      "50000/50000 [==============================] - 2s 40us/step - loss: 2.0861 - acc: 0.2686 - val_loss: 2.0698 - val_acc: 0.2685\n",
      "Epoch 7/60\n",
      "50000/50000 [==============================] - 2s 39us/step - loss: 2.0537 - acc: 0.2761 - val_loss: 2.0405 - val_acc: 0.2734\n",
      "Epoch 8/60\n",
      "50000/50000 [==============================] - 2s 40us/step - loss: 2.0270 - acc: 0.2826 - val_loss: 2.0164 - val_acc: 0.2731\n",
      "Epoch 9/60\n",
      "50000/50000 [==============================] - 2s 40us/step - loss: 2.0042 - acc: 0.2865 - val_loss: 1.9949 - val_acc: 0.2907\n",
      "Epoch 10/60\n",
      "50000/50000 [==============================] - 2s 40us/step - loss: 1.9848 - acc: 0.2929 - val_loss: 1.9763 - val_acc: 0.2930\n",
      "Epoch 11/60\n",
      "50000/50000 [==============================] - 2s 40us/step - loss: 1.9676 - acc: 0.2987 - val_loss: 1.9616 - val_acc: 0.3052\n",
      "Epoch 12/60\n",
      "50000/50000 [==============================] - 2s 42us/step - loss: 1.9526 - acc: 0.3052 - val_loss: 1.9461 - val_acc: 0.3104\n",
      "Epoch 13/60\n",
      "50000/50000 [==============================] - 2s 40us/step - loss: 1.9389 - acc: 0.3108 - val_loss: 1.9327 - val_acc: 0.3146\n",
      "Epoch 14/60\n",
      "50000/50000 [==============================] - 2s 41us/step - loss: 1.9268 - acc: 0.3165 - val_loss: 1.9214 - val_acc: 0.3200\n",
      "Epoch 15/60\n",
      "50000/50000 [==============================] - 2s 41us/step - loss: 1.9155 - acc: 0.3223 - val_loss: 1.9115 - val_acc: 0.3209\n",
      "Epoch 16/60\n",
      "50000/50000 [==============================] - 2s 40us/step - loss: 1.9056 - acc: 0.3255 - val_loss: 1.9029 - val_acc: 0.3229\n",
      "Epoch 17/60\n",
      "50000/50000 [==============================] - 2s 42us/step - loss: 1.8965 - acc: 0.3308 - val_loss: 1.8921 - val_acc: 0.3268\n",
      "Epoch 18/60\n",
      "50000/50000 [==============================] - 2s 46us/step - loss: 1.8880 - acc: 0.3329 - val_loss: 1.8845 - val_acc: 0.3393\n",
      "Epoch 19/60\n",
      "50000/50000 [==============================] - 2s 43us/step - loss: 1.8799 - acc: 0.3352 - val_loss: 1.8766 - val_acc: 0.3378\n",
      "Epoch 20/60\n",
      "50000/50000 [==============================] - 3s 57us/step - loss: 1.8728 - acc: 0.3381 - val_loss: 1.8685 - val_acc: 0.3401\n",
      "Epoch 21/60\n",
      "50000/50000 [==============================] - 3s 51us/step - loss: 1.8652 - acc: 0.3431 - val_loss: 1.8616 - val_acc: 0.3451\n",
      "Epoch 22/60\n",
      "50000/50000 [==============================] - 3s 56us/step - loss: 1.8582 - acc: 0.3457 - val_loss: 1.8552 - val_acc: 0.3389\n",
      "Epoch 23/60\n",
      "50000/50000 [==============================] - 3s 55us/step - loss: 1.8513 - acc: 0.3476 - val_loss: 1.8489 - val_acc: 0.3473\n",
      "Epoch 24/60\n",
      "50000/50000 [==============================] - 2s 44us/step - loss: 1.8445 - acc: 0.3503 - val_loss: 1.8416 - val_acc: 0.3550\n",
      "Epoch 25/60\n",
      "50000/50000 [==============================] - 2s 43us/step - loss: 1.8378 - acc: 0.3529 - val_loss: 1.8347 - val_acc: 0.3561\n",
      "Epoch 26/60\n",
      "50000/50000 [==============================] - 2s 45us/step - loss: 1.8314 - acc: 0.3544 - val_loss: 1.8275 - val_acc: 0.3566\n",
      "Epoch 27/60\n",
      "50000/50000 [==============================] - 2s 48us/step - loss: 1.8250 - acc: 0.3570 - val_loss: 1.8224 - val_acc: 0.3614\n",
      "Epoch 28/60\n",
      "50000/50000 [==============================] - 2s 44us/step - loss: 1.8187 - acc: 0.3586 - val_loss: 1.8156 - val_acc: 0.3615\n",
      "Epoch 29/60\n",
      "50000/50000 [==============================] - 2s 43us/step - loss: 1.8123 - acc: 0.3608 - val_loss: 1.8108 - val_acc: 0.3674\n",
      "Epoch 30/60\n",
      "50000/50000 [==============================] - 2s 46us/step - loss: 1.8062 - acc: 0.3630 - val_loss: 1.8034 - val_acc: 0.3646\n",
      "Epoch 31/60\n",
      "50000/50000 [==============================] - 2s 45us/step - loss: 1.8008 - acc: 0.3640 - val_loss: 1.7986 - val_acc: 0.3657\n",
      "Epoch 32/60\n",
      "50000/50000 [==============================] - 2s 44us/step - loss: 1.7954 - acc: 0.3670 - val_loss: 1.7922 - val_acc: 0.3675\n",
      "Epoch 33/60\n",
      "50000/50000 [==============================] - 2s 42us/step - loss: 1.7899 - acc: 0.3686 - val_loss: 1.7869 - val_acc: 0.3682\n",
      "Epoch 34/60\n",
      "50000/50000 [==============================] - 2s 45us/step - loss: 1.7845 - acc: 0.3698 - val_loss: 1.7812 - val_acc: 0.3737\n",
      "Epoch 35/60\n",
      "50000/50000 [==============================] - 2s 46us/step - loss: 1.7795 - acc: 0.3706 - val_loss: 1.7768 - val_acc: 0.3766\n",
      "Epoch 36/60\n",
      "50000/50000 [==============================] - 2s 42us/step - loss: 1.7742 - acc: 0.3722 - val_loss: 1.7718 - val_acc: 0.3721\n",
      "Epoch 37/60\n",
      "50000/50000 [==============================] - 2s 45us/step - loss: 1.7696 - acc: 0.3742 - val_loss: 1.7673 - val_acc: 0.3746\n",
      "Epoch 38/60\n",
      "50000/50000 [==============================] - 2s 44us/step - loss: 1.7646 - acc: 0.3765 - val_loss: 1.7639 - val_acc: 0.3697\n",
      "Epoch 39/60\n",
      "50000/50000 [==============================] - 2s 46us/step - loss: 1.7603 - acc: 0.3766 - val_loss: 1.7566 - val_acc: 0.3808\n",
      "Epoch 40/60\n",
      "50000/50000 [==============================] - 2s 42us/step - loss: 1.7557 - acc: 0.3790 - val_loss: 1.7530 - val_acc: 0.3811\n",
      "Epoch 41/60\n",
      "50000/50000 [==============================] - 2s 42us/step - loss: 1.7512 - acc: 0.3804 - val_loss: 1.7476 - val_acc: 0.3834\n",
      "Epoch 42/60\n",
      "50000/50000 [==============================] - 2s 46us/step - loss: 1.7469 - acc: 0.3812 - val_loss: 1.7437 - val_acc: 0.3833\n",
      "Epoch 43/60\n",
      "50000/50000 [==============================] - 2s 48us/step - loss: 1.7425 - acc: 0.3826 - val_loss: 1.7393 - val_acc: 0.3876\n",
      "Epoch 44/60\n",
      "50000/50000 [==============================] - 2s 46us/step - loss: 1.7380 - acc: 0.3856 - val_loss: 1.7362 - val_acc: 0.3870\n",
      "Epoch 45/60\n",
      "50000/50000 [==============================] - 2s 46us/step - loss: 1.7341 - acc: 0.3865 - val_loss: 1.7307 - val_acc: 0.3881\n",
      "Epoch 46/60\n",
      "50000/50000 [==============================] - 2s 47us/step - loss: 1.7299 - acc: 0.3870 - val_loss: 1.7269 - val_acc: 0.3917\n",
      "Epoch 47/60\n",
      "50000/50000 [==============================] - 2s 48us/step - loss: 1.7254 - acc: 0.3888 - val_loss: 1.7253 - val_acc: 0.3899\n",
      "Epoch 48/60\n",
      "50000/50000 [==============================] - 2s 49us/step - loss: 1.7216 - acc: 0.3905 - val_loss: 1.7190 - val_acc: 0.3896\n",
      "Epoch 49/60\n",
      "50000/50000 [==============================] - 2s 46us/step - loss: 1.7177 - acc: 0.3927 - val_loss: 1.7144 - val_acc: 0.3954\n",
      "Epoch 50/60\n",
      "50000/50000 [==============================] - 2s 48us/step - loss: 1.7133 - acc: 0.3932 - val_loss: 1.7108 - val_acc: 0.3913\n",
      "Epoch 51/60\n",
      "50000/50000 [==============================] - 2s 48us/step - loss: 1.7096 - acc: 0.3960 - val_loss: 1.7077 - val_acc: 0.3975\n",
      "Epoch 52/60\n",
      "50000/50000 [==============================] - 2s 49us/step - loss: 1.7057 - acc: 0.3967 - val_loss: 1.7036 - val_acc: 0.3992\n",
      "Epoch 53/60\n",
      "50000/50000 [==============================] - 2s 50us/step - loss: 1.7016 - acc: 0.3978 - val_loss: 1.6998 - val_acc: 0.4012\n",
      "Epoch 54/60\n",
      "50000/50000 [==============================] - 2s 47us/step - loss: 1.6974 - acc: 0.3994 - val_loss: 1.6949 - val_acc: 0.4033\n",
      "Epoch 55/60\n",
      "50000/50000 [==============================] - 2s 50us/step - loss: 1.6938 - acc: 0.4008 - val_loss: 1.6905 - val_acc: 0.4011\n",
      "Epoch 56/60\n",
      "50000/50000 [==============================] - 2s 50us/step - loss: 1.6897 - acc: 0.4019 - val_loss: 1.6893 - val_acc: 0.4035\n",
      "Epoch 57/60\n",
      "50000/50000 [==============================] - 2s 46us/step - loss: 1.6860 - acc: 0.4037 - val_loss: 1.6837 - val_acc: 0.4048\n",
      "Epoch 58/60\n",
      "50000/50000 [==============================] - 2s 46us/step - loss: 1.6821 - acc: 0.4045 - val_loss: 1.6802 - val_acc: 0.4047\n",
      "Epoch 59/60\n",
      "50000/50000 [==============================] - 2s 50us/step - loss: 1.6784 - acc: 0.4064 - val_loss: 1.6755 - val_acc: 0.4072\n",
      "Epoch 60/60\n",
      "50000/50000 [==============================] - 3s 57us/step - loss: 1.6749 - acc: 0.4074 - val_loss: 1.6734 - val_acc: 0.4098\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x119700780>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train,\n",
    "          batch_size=100,\n",
    "          epochs=60,\n",
    "          verbose=1,\n",
    "          validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After 60 epochs, our network only has an accuracy of 40%. Still better than random guesses (10%) but 40% is terrible. The current record for CIFAR-10 accuracy is 97%. So we have a long way to go! \n",
    "\n",
    "In the next notebook, we will introduce convolutional neural networks, which will greatly improve our performance."
   ]
  }
 ],
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